This paper describes a new technique for training feedforward neural networks . We employ the proposed algorithm for robust neural network training purpose . Dissecting the fluctuation into biologically relevant components is important for understanding how a cell controls and harnesses noise and how information is transferred over apparently noisy intracellular networks . While substantial theoretical and experimental advancement on the decomposition of fluctuation was achieved for feedforward networks without any loop , we still lack a theoretical basis that can consistently extend such advancement to feedback networks . The main obstacle that hampers is the circulative propagation of fluctuation by feedback loops . A state-space representation is considered for a neural identifier . The recurrent network configuration is obtained by a combination of feedforward network architectures with dynamical elements in the form of stable filters . To guarantee the boundedness of the states , joint PD control is introduced in the system . We have discovered , however , that redundancy can qualitatively change the computations carried out by a network . We prove that for both feedforward and feedback networks the simple duplication of nodes and connections results in more accurate , faster , and more stable computation . MATERIALS AND METHODS We treated the task of assigning Positive Valence severity as a text classification problem . During development , we experimented with regularized multinomial logistic regression classifiers , gradient boosted trees , and feedforward , fully-connected neural networks . We found both regularization and feature selection via mutual information to be very important in preventing models from overfitting the data . We model a part of a process in pulp to paper production using feed forward connected neural networks . A set of parameters related to paper quality is predicted from a set of process values . Abstract We report new results on the corner classification approach to training feedback forward neural networks . It is shown that a prescriptive learning procedure where the weights are simply read off based on the training data can provide good generalization . Part 1 Introduction - structures , dynamics , and learning : introduction , A.J.Maren history , C.T.Harston biological basis , C.T.Harston structures , A.J.Maren dynamics , A.J.Maren learning , C.T.Harston . Part 2 Neural network systems : multilayer , feedforward networks - delta learning rule , A.J.Maren multilayer , feedforward networks - optimizing learning rule , A.J.Maren single-layer , laterally connected networks , A.J.Maren single-layer , topologically organised networks , A.J.Maren bilayer , feedforward/feedback networks , A.J.Maren multilayer , co-operative/competitive networks , A.J.Maren systems of interacting networks , A.J.Maren . Part 3 Implementing neural networks : system design , D.Jones and S.Franklin configuring and optimizing feedforward networks , A.J.Maren et al hardware implementations , S.Morgan and C.R.Parten . subsection : Implicit High - order Interactions FNN , Deep Crossing , and the deep part in Wide & Deep exploit a feed - forward neural network on the field embedding vector to learn high - order feature interactions . The forward process is : where is the layer depth , is an activation function , and is the output of the - th layer . Our results are competitive with the best systems , achieving a new state of the art on the recent TriviaQA benchmarks . Our success on this task is especially noteworthy because the task - specific architecture is a simple reading architecture , in particular a single layer BiLSTM with a feed - forward neural network for span prediction . Finally , we provide an analysis demonstrating that our systems are able to exploit background knowledge in a semantically appropriate manner ( § [ reference ] ) . subsection : Encoder Stack Similar to the autoregressive Transformer , both the encoder and decoder stacks are composed entirely of feed - forward networks ( MLPs ) and multi - head attention modules . Since no RNNs are used , there is no inherent requirement for sequential execution , making non - autoregressive decoding possible . The neural counterpart to alignment , attention , which is a key part of our approach , was originally proposed and has been predominantly used in conjunction with LSTMs and to a lesser extent with CNNs . In contrast , our use of attention is purely based on word embeddings and our method essentially consists of feed - forward networks that operate largely independently of word order . section : Approach One might speculate that it is the recurrent nature of these models that enables these results . In this work we demonstrate that simple feed - forward networks without any recurrence can achieve comparable or better accuracies than LSTMs , as long as they are globally normalized . Our model , described in detail in Section [ reference ] , uses a transition system and feature embeddings as introduced by chen - manning:2014:EMNLP . In the original formulation was defined by taking an ‘ acoustic ’ distribution from the CTC network , a ‘ linguistic ’ distribution from the prediction network , then multiplying the two together and renormalising . An improvement introduced in this paper is to instead feed the hidden activations of both networks into a separate feedforward output network , whose outputs are then normalised with a softmax function to yield . This allows a richer set of possibilities for combining linguistic and acoustic information , and appears to lead to better generalisation . [ reference ] . It is also used in the cross - entropy loss 2 for multi - class classification . For example , it has been used in recurrent neural networks for session - based sequential recommendation [ reference ][ reference ][ reference ][ reference ][ reference ] and in feedward neural networks applied to Youtube recommendation [ reference ] . Note that the Inference Network only appears in the Egan - Ent - VI model . For experiments with the synthetic datasets , the following fully - connected feed forward neural networks are employed where FC and BN denote fully - connected layer and batch normalization layer respectively . Note that since the input noise to the generator has dimension 4 , the Inference Net output has dimension 4 * 2 , where the first 4 elements correspond the inferred mean , and the last 4 elements correspond to the inferred diagonal covariance matrix in log scale . Recent research in on-chip multicast support has proposed forking of broadcast/multicast packets within the network at the router buffers , but these techniques are far from ideal , since they increase buffer occupancy which lowers throughput , and packets incur delay and power penalties at each router . In this work , we analyze an ideal broadcast mesh ; show the substantial gaps between state-of-the-art multicast NoCs and the ideal ; then propose BOOM , which comprises a WHIRL routing protocol that ideally load balances broadcast traffic , a mXbar multicast crossbar circuit that enables multicast traversal at similar energy-delay as unicasts , and speculative bypassing of buffering for multicast flits . Together , they enable broadcast packets to approach the delay , energy , and throughput of the ideal fabric . The present invention relates to a connection structure and the boom construction method for PSC continuous strengthening box girder bridges . The boom structure comprises a connecting piece , connecting means and disposed in the compositions steel deck center line to change the power system and receiving the continuous PSC stress condition by adding the boom girder bridge , thereby increasing its carrying capacity . Combination steel so that the top plate is anchored to the box girder by connecting means . This paper presents an alternative boom design for mobile cranes and a method to produce it for minimizing the energy consumption during its production . The main change in the production of the crane booms is the shape of the booms . By combining their properties in other ways , more data structures can be made , like mobiles . The paper defines the data structures of this extended Boom Hierarchy and shows how the functions reduce , map , and filter are applied to them . By combining their properties in other ways , more data structures can be made , like mobiles . The paper defines the data structures of this extended Boom Hierarchy and shows how the functions reduce , map , and filter are applied to them . The present invention relates to a boom structure that achieves cost reduction and shortened manufacturing time in the design and manufacture of the entire turning device by installing the number of slew bearings for the turning device of the burner boom in a single number , and the pedestal structure is simplified and miniaturized . There is an advantage , and the distance between the two foundations is too long to match the axis of rotation while matching the accuracy ( accuracy ) was a very difficult task , but the work itself to match the previous axis of rotation to be replaced by a single foundation by the present invention is required In addition , the pedestal fabrication avoids the existing large cylindrical structure , which eliminates the need for bending , rolling , pipework , etc . , significantly reducing manufacturing costs , and does not expose the cylinder rod to seawater and dust when the burner boom is not in operation . The scheme was tested and the results proved to be accurate . We then compared the computational simulation and the laboratory experiment of an innovative boom arrangement . Satisfactory results indicate the potential for using the computational technique developed in this paper to aid in oil boom design as well as in other multilayer immiscible flow applications . A new deployable boom concept invented by AFRL has recently been gaining strong attention by those in the MicroSat and CubeSat communities . The Triangular Rollable And Collapsible ( TRAC ) mast has been demonstrated to enable new planar payloads and thus new mission concepts due to its extremely efficient packaging scheme . The present invention discloses a boom-type full hydraulic concrete automatic feeding system was stirred for one cloth feed , mainly for conventional concrete mixing and conveying concrete construction methods poor quality , high-cost design . The present invention is fully hydraulic boom-type automatic feeding concrete stirred integrally cloth feeding system comprising a vehicle chassis , the chassis is provided on the vehicle chassis , in the feeding device are provided on said frame , a stirring device , a pumping means , the boom means , cleaning means and an electrical control box , wherein said feeding means to said feeding means the boom stirring means , said stirring means to feed the pumping means , the pumping means to feed , a concrete fabric by means of the boom . The present invention automatically feeding continuously forced stirring , pumping , cloth and other functions into one , a high degree of automation , machine mounted on a vehicle chassis , mobile flexibility , reasonable structure , convenient maintenance , low cost . However , disturbances , including tanker wake and atmospheric turbulence , can disturb the motion of the flying boom . In addition , the boom operator requires that the flying boom has good handling qualities to accomplish aerial boom refuelling . To solve the first challenge , a novel back-stepping control approach – back-stepping control driven by a sliding mode disturbance observer – was adopted for attitude control to improve robustness and performance . We show via examples many situations in which our theorems can be applied . This includes a detailed analysis of distributive laws for members of an extension of the Boom type hierarchy , well known to functional programmers . Abstract Effects of noises and some other network properties on the weak signal propagation are studied systematically in feedforward acupuncture networks ( FFN ) based on FitzHugh – Nagumo neuron model . It is found that noises with medium intensity can enhance signal propagation and this effect can be further increased by the feedforward network structure . Resonant properties in the noisy network can also be altered by several network parameters , such as heterogeneity , synapse features , and feedback connections . The study presents a neural network , which uses filters based on logistic mapping ( LogNNet ) . LogNNet has a feedforward network structure , but possesses the properties of reservoir neural networks . The input weight matrix , set by a recurrent logistic mapping , forms the kernels that transform the input space to the higher-dimensional feature space . This paper suggests an evolving hybrid neural fuzzy network ( eHFN ) modeling approach for realized volatility forecasting with jumps . The eHFN model is nonlinear , time-raying , and uses neurons based on uninorms and sigmoidal activation functions in a feedforward network topology . The approach simultaneously chooses the number of hidden layer neurons and corresponding neural networks weights . The proposed model contains a convolutional network component to extract high-level features and a recurrent network component to enhance the modeling of the temporal characteristics of TS data . In addition , a feedforward fully connected network with the sparse group lasso ( SGL ) regularization is used to generate the final classification . The proposed architecture not only achieves satisfying classification accuracy , but also obtains good interpretability through the SGL regularization . The main goal of this project is to apply differentiable and interactive t-norms to perform the conjunction and disjunction operations in a fuzzy neural network , to pattern classification and rule generation . The differentiable fuzzy neural network , denominated RNDD , object of this dissertation , is a feedforward fully-connected network with three layers : the input layer , the hidden layer of and neurons and the output layer of or neurons . It was used the backpropagation algorithm for training . Finally , we present performance evaluation results and comparisons using both synthetic and real array data . In the case of feedforward architectures , the direction finding problem is formulated as a mapping problem , and we establish how a feedforward type network can be used to approximate the desired mapping . The network is trained on a representative set of input-output pairs . Based on the analysis , it is found that the estimated annually electricity cost for HV Laboratory and EM Laboratory are RM 392.00 and RM 3197.76 respectively . For prediction of energy consumption of the two laboratories , Artificial Neural Network ( ANN ) algorithm is applied as computational tool using feedforward network type . The results show that the ANN is successfully modelled to predict the energy consumption . The query encoder is implemented by another bidirectional GRU network . This time the last hidden state of the forward network is concatenated with the last hidden state of the backward network to form the query embedding , that is . The word embedding function is implemented in a usual way as a look - up table . In this paper , we closely follow the neural translation model proposed in bahdanau2014neural and use a bidirectional recurrent neural network , which consists of two recurrent neural networks . The forward network reads the input sentence in a forward direction : where is a continuous embedding of the - th input symbol , and is a recurrent activation function . Similarly , the reverse network reads the sentence in a reverse direction ( right to left ) : At each location in the input sentence , we concatenate the hidden states from the forward and reverse RNNs to form a context set where . Abstract We present a new network topology to avoid overfitting in two-layered feedforward networks . We use two additional linear layers and principal component analysis to reduce the dimension of both inputs and internal representations and to transmit the essential information . Thereby neurons with small variance in the output are removed , which results in better generalization properties . Our method performs far better than many traditional artificial feature extraction methods with the help of standalone filter learning and multiscale local feature combination . Such structure cascaded by both linear layers with convolution filters and non-linear layers in binarization process shows better adaptability in different databases . With the help of parallel computing , training time is much shorter than PCANet and also more fixed compared to convolutional neural network . In this paper , we propose three new methods to improve Matsui ’s branch-and-bound search algorithm which is known as the first generic algorithm for finding the best differential and linear trails . The three methods , named Reconstructing DDT and LAT According to Weight , Executing Linear Layer Operations in Minimal Cost and Merging Two 4-bit S-boxes into One 8-bit S-box respectively , can efficiently speed up the search process by reducing the search space as much as possible and reducing the cost of executing linear layer operations . We apply our improved algorithm to DESL and GIFT , which are still the hard instances for the automatic search methods . This memory network is constructed to incorporate new information using a self-attention mechanism over the memory and input vectors to return a corresponding output vector for every timestep . Consequently , we obtain 3 output vectors which are then multiplied element-wisely into a single one ; and finally , we feed this vector to a linear neural layer to produce a scalar score for the triple . Experimental results show that our proposed R-MeN obtains state-of-the-art results on two well-known benchmark datasets WN11 and FB13 for triple classification task . The parameters of the fuzzy layer of R-ELANFIS are randomly selected by incorporating the explicit knowledge representation using fuzzy membership functions . The parameters of the linear neural layer are determined by solving a constrained optimization problem in a regularized framework . Simulations on regression problems show that R-ELANFIS achieves similar or better generalization performance compared to well known kernel based regression methods and ELM based neuro-fuzzy systems . We propose a novel feature based domain adaptation technique using hidden layer factorisation . This method is fundamentally different from existing methods because we use the domain features to calculate a linear combination of linear layers . These linear layers can capture domain specific information and information common to different domains . The encoded low-level and high-level features are then combined by concatenation and convolution . Finally , a linear fully connected layer is used to evaluate the saliency of a queried region . A full resolution saliency map is obtained by querying the saliency of each local region of an image . Time-delay and linear layer ( design ) intelligent computing expert system models were developed for predicting shelf life of soft mouth melting milk cakes stored at 6C . Linear layer ( design ) model gave best outcomes ( MSE : 0.000293366 , RMSE : 0.017127919 , R : 0.996479613 ) . Regression equations were developed based on these outcomes , shelf life was predicted 49.54 days . In this paper , we propose a four stage method engineering process which aims at estimating impact of change . The process model described is a linear layered model . Impact sets are computed by analyzing dependency tractability relations with other connected method components . In this paper , the neural network composed of a chaotic neuron layer and a linear neuron layer is used to construct a block cipher that transforms the data from the plaintext form into the unintelligible form under the control of the key . Among them , the chaotic neuron layer realizes data diffusion , the linear neuron layer realizes data confusion , and the two layers are repeated for several times to strengthen the cipher . The decryption process is symmetric to the encryption process . Unfortunately , least squares estimation does not give a maximum likelihood ( ML ) estimate of the weights in the network . Logistic regression , on the other hand , gives ML estimates for single layer linear models only . This report describes how to obtain ML estimates of the weights in a multi-layer model , and compares LS-BP to ML-BP using several examples . Link prediction is a task of inferring missing facts based on existing ones . We propose TuckER , a relatively simple but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples . TuckER outperforms all previous state - of - the - art models across standard link prediction datasets . For web - scale recommender systems ( RSs ) , the input features are usually sparse , categorical - continuous - mixed , and high - dimensional . Linear models , such as logistic regression with FTRL , are widely adopted as they are easy to manage , maintain , and deploy . Because linear models lack the ability of learning feature interactions , data scientists have to spend a lot of work on engineering cross features in order to achieve better performance . Models that are easy to optimize are easy to perturb . Linear models lack the capacity to resist adversarial perturbation ; only structures with a hidden layer ( where the universal approximator theorem applies ) should be trained to resist adversarial perturbation . RBF networks are resistant to adversarial examples . In the meta - BiLSTM model , we concatenate the output , for each word , of its context sensitive character and word - based encodings , and put this through another BiLSTM to create an additional combined context sensitive encoding . This is followed by a final MLP whose output is passed to a linear layer for tag prediction . With this setup , each of the models can be optimized independently which we describe in more detail in § 3.5 . For all experiments , we trained both seq2seq and BSO models with mini - batch Adagrad ( using batches of size 64 ) , and we renormalized all gradients so they did not exceed 5 before updating parameters . We did not extensively tune learning - rates , but we found initial rates of 0.02 for the encoder and decoder LSTMs , and a rate of 0.1 or 0.2 for the final linear layer ( i.e. , the layer tasked with making word - predictions at each time - step ) to work well across all the tasks we considered . Code implementing the experiments described below can be found at . The translation model computes a distribution over the V possible target words y i + 1 by transforming the LSTM output h i via a linear layer with weights W o and bias Thus , a reasonable compromise was to add a small correction factor which is learned per word , such that : where is a matrix projecting a low - dimensional embedding vector back up to the dimensionality of the projected LSTM hidden state of . This amounts to adding a bottleneck linear layer , and brings the CNN Softmax much closer to our best result , as can be seen in Table [ reference ] , where adding a 128 - dim correction halves the gap between regular and the CNN Softmax . Aside from a big reduction in the number of parameters and incorporating morphological knowledge from words , the other benefit of this approach is that out - of - vocabulary ( OOV ) words can easily be scored . All these layers are separated by Rectifier Linear Units ( ReLu ) . Finally , a fully - connected linear layer projects to the output of the network , i.e. , the Q - values . The optimization employed to train the network is RMSProp ( with momentum parameter ) . use this - localized convolution to define a convolutional neural network on graphs . subsection : Layer - Wise Linear Model A neural network model based on graph convolutions can therefore be built by stacking multiple convolutional layers of the form of Eq . Note that all of these results are still lower than BM25 , which shows that they are not good at learning from weak supervision signals for ranking . We have also examined the score model with a network with a single linear hidden layer , with the embedding vector representation , which is equivalent to a linear regression model with the ability of representation learning . Comparing the results of this experiment with Score - Embed in Table [ reference ] , we can see that with a single - linear network we are not able to achieve a performance that is as good as a deep neural network with non - linearity . — see for recent reviews of the different contexts and applications . The network architecture in the classification stage is that of a three layer neural network comprised from an input layer , a hidden layer of nonlinear units , and a linear output layer . The input weights are randomly chosen and untrained , and the output weights are trained in a single batch using least squares regression . FWN is consisted with a Weight Layer and a nonlinear transformation , which decides the importance of each dimension in the part feature vector . Considering that a single linear Weight Layer might cause excessive response on some specific dimensions of the part vector , we add a nonlinear function to equalize the response of part feature vector , and the fused feature representation is where the and the are the global and part feature vectors . The and in Eq . First , the network structures are substantially different . Rainbow uses noisy linear layers and ReLU activations throughout the network , whereas Reactor uses standard linear layers and concatenated ReLU activations throughout . To overcome partial observability , Rainbow , inheriting this choice from DQN , uses frame stacking .
This paper describes a new technique for training feedforward neural networks . We employ the proposed algorithm for robust neural network training purpose .
Dissecting the fluctuation into biologically relevant components is important for understanding how a cell controls and harnesses noise and how information is transferred over apparently noisy intracellular networks . While substantial theoretical and experimental advancement on the decomposition of fluctuation was achieved for feedforward networks without any loop , we still lack a theoretical basis that can consistently extend such advancement to feedback networks . The main obstacle that hampers is the circulative propagation of fluctuation by feedback loops .
A state-space representation is considered for a neural identifier . The recurrent network configuration is obtained by a combination of feedforward network architectures with dynamical elements in the form of stable filters . To guarantee the boundedness of the states , joint PD control is introduced in the system .
We have discovered , however , that redundancy can qualitatively change the computations carried out by a network . We prove that for both feedforward and feedback networks the simple duplication of nodes and connections results in more accurate , faster , and more stable computation .
MATERIALS AND METHODS We treated the task of assigning Positive Valence severity as a text classification problem . During development , we experimented with regularized multinomial logistic regression classifiers , gradient boosted trees , and feedforward , fully-connected neural networks . We found both regularization and feature selection via mutual information to be very important in preventing models from overfitting the data .
We model a part of a process in pulp to paper production using feed forward connected neural networks . A set of parameters related to paper quality is predicted from a set of process values .
Abstract We report new results on the corner classification approach to training feedback forward neural networks . It is shown that a prescriptive learning procedure where the weights are simply read off based on the training data can provide good generalization .
Part 1 Introduction - structures , dynamics , and learning : introduction , A.J.Maren history , C.T.Harston biological basis , C.T.Harston structures , A.J.Maren dynamics , A.J.Maren learning , C.T.Harston . Part 2 Neural network systems : multilayer , feedforward networks - delta learning rule , A.J.Maren multilayer , feedforward networks - optimizing learning rule , A.J.Maren single-layer , laterally connected networks , A.J.Maren single-layer , topologically organised networks , A.J.Maren bilayer , feedforward/feedback networks , A.J.Maren multilayer , co-operative/competitive networks , A.J.Maren systems of interacting networks , A.J.Maren . Part 3 Implementing neural networks : system design , D.Jones and S.Franklin configuring and optimizing feedforward networks , A.J.Maren et al hardware implementations , S.Morgan and C.R.Parten .
subsection : Implicit High - order Interactions FNN , Deep Crossing , and the deep part in Wide & Deep exploit a feed - forward neural network on the field embedding vector to learn high - order feature interactions . The forward process is : where is the layer depth , is an activation function , and is the output of the - th layer .
Our results are competitive with the best systems , achieving a new state of the art on the recent TriviaQA benchmarks . Our success on this task is especially noteworthy because the task - specific architecture is a simple reading architecture , in particular a single layer BiLSTM with a feed - forward neural network for span prediction . Finally , we provide an analysis demonstrating that our systems are able to exploit background knowledge in a semantically appropriate manner ( § [ reference ] ) .
subsection : Encoder Stack Similar to the autoregressive Transformer , both the encoder and decoder stacks are composed entirely of feed - forward networks ( MLPs ) and multi - head attention modules . Since no RNNs are used , there is no inherent requirement for sequential execution , making non - autoregressive decoding possible .
The neural counterpart to alignment , attention , which is a key part of our approach , was originally proposed and has been predominantly used in conjunction with LSTMs and to a lesser extent with CNNs . In contrast , our use of attention is purely based on word embeddings and our method essentially consists of feed - forward networks that operate largely independently of word order . section : Approach
One might speculate that it is the recurrent nature of these models that enables these results . In this work we demonstrate that simple feed - forward networks without any recurrence can achieve comparable or better accuracies than LSTMs , as long as they are globally normalized . Our model , described in detail in Section [ reference ] , uses a transition system and feature embeddings as introduced by chen - manning:2014:EMNLP .
In the original formulation was defined by taking an ‘ acoustic ’ distribution from the CTC network , a ‘ linguistic ’ distribution from the prediction network , then multiplying the two together and renormalising . An improvement introduced in this paper is to instead feed the hidden activations of both networks into a separate feedforward output network , whose outputs are then normalised with a softmax function to yield . This allows a richer set of possibilities for combining linguistic and acoustic information , and appears to lead to better generalisation .
[ reference ] . It is also used in the cross - entropy loss 2 for multi - class classification . For example , it has been used in recurrent neural networks for session - based sequential recommendation [ reference ][ reference ][ reference ][ reference ][ reference ] and in feedward neural networks applied to Youtube recommendation [ reference ] .
Note that the Inference Network only appears in the Egan - Ent - VI model . For experiments with the synthetic datasets , the following fully - connected feed forward neural networks are employed where FC and BN denote fully - connected layer and batch normalization layer respectively . Note that since the input noise to the generator has dimension 4 , the Inference Net output has dimension 4 * 2 , where the first 4 elements correspond the inferred mean , and the last 4 elements correspond to the inferred diagonal covariance matrix in log scale .
Recent research in on-chip multicast support has proposed forking of broadcast/multicast packets within the network at the router buffers , but these techniques are far from ideal , since they increase buffer occupancy which lowers throughput , and packets incur delay and power penalties at each router . In this work , we analyze an ideal broadcast mesh ; show the substantial gaps between state-of-the-art multicast NoCs and the ideal ; then propose BOOM , which comprises a WHIRL routing protocol that ideally load balances broadcast traffic , a mXbar multicast crossbar circuit that enables multicast traversal at similar energy-delay as unicasts , and speculative bypassing of buffering for multicast flits . Together , they enable broadcast packets to approach the delay , energy , and throughput of the ideal fabric .
The present invention relates to a connection structure and the boom construction method for PSC continuous strengthening box girder bridges . The boom structure comprises a connecting piece , connecting means and disposed in the compositions steel deck center line to change the power system and receiving the continuous PSC stress condition by adding the boom girder bridge , thereby increasing its carrying capacity . Combination steel so that the top plate is anchored to the box girder by connecting means .
This paper presents an alternative boom design for mobile cranes and a method to produce it for minimizing the energy consumption during its production . The main change in the production of the crane booms is the shape of the booms .
By combining their properties in other ways , more data structures can be made , like mobiles . The paper defines the data structures of this extended Boom Hierarchy and shows how the functions reduce , map , and filter are applied to them .
By combining their properties in other ways , more data structures can be made , like mobiles . The paper defines the data structures of this extended Boom Hierarchy and shows how the functions reduce , map , and filter are applied to them .
The present invention relates to a boom structure that achieves cost reduction and shortened manufacturing time in the design and manufacture of the entire turning device by installing the number of slew bearings for the turning device of the burner boom in a single number , and the pedestal structure is simplified and miniaturized . There is an advantage , and the distance between the two foundations is too long to match the axis of rotation while matching the accuracy ( accuracy ) was a very difficult task , but the work itself to match the previous axis of rotation to be replaced by a single foundation by the present invention is required In addition , the pedestal fabrication avoids the existing large cylindrical structure , which eliminates the need for bending , rolling , pipework , etc . , significantly reducing manufacturing costs , and does not expose the cylinder rod to seawater and dust when the burner boom is not in operation .
The scheme was tested and the results proved to be accurate . We then compared the computational simulation and the laboratory experiment of an innovative boom arrangement . Satisfactory results indicate the potential for using the computational technique developed in this paper to aid in oil boom design as well as in other multilayer immiscible flow applications .
A new deployable boom concept invented by AFRL has recently been gaining strong attention by those in the MicroSat and CubeSat communities . The Triangular Rollable And Collapsible ( TRAC ) mast has been demonstrated to enable new planar payloads and thus new mission concepts due to its extremely efficient packaging scheme .
The present invention discloses a boom-type full hydraulic concrete automatic feeding system was stirred for one cloth feed , mainly for conventional concrete mixing and conveying concrete construction methods poor quality , high-cost design . The present invention is fully hydraulic boom-type automatic feeding concrete stirred integrally cloth feeding system comprising a vehicle chassis , the chassis is provided on the vehicle chassis , in the feeding device are provided on said frame , a stirring device , a pumping means , the boom means , cleaning means and an electrical control box , wherein said feeding means to said feeding means the boom stirring means , said stirring means to feed the pumping means , the pumping means to feed , a concrete fabric by means of the boom . The present invention automatically feeding continuously forced stirring , pumping , cloth and other functions into one , a high degree of automation , machine mounted on a vehicle chassis , mobile flexibility , reasonable structure , convenient maintenance , low cost .
However , disturbances , including tanker wake and atmospheric turbulence , can disturb the motion of the flying boom . In addition , the boom operator requires that the flying boom has good handling qualities to accomplish aerial boom refuelling . To solve the first challenge , a novel back-stepping control approach – back-stepping control driven by a sliding mode disturbance observer – was adopted for attitude control to improve robustness and performance .
We show via examples many situations in which our theorems can be applied . This includes a detailed analysis of distributive laws for members of an extension of the Boom type hierarchy , well known to functional programmers .
Abstract Effects of noises and some other network properties on the weak signal propagation are studied systematically in feedforward acupuncture networks ( FFN ) based on FitzHugh – Nagumo neuron model . It is found that noises with medium intensity can enhance signal propagation and this effect can be further increased by the feedforward network structure . Resonant properties in the noisy network can also be altered by several network parameters , such as heterogeneity , synapse features , and feedback connections .
The study presents a neural network , which uses filters based on logistic mapping ( LogNNet ) . LogNNet has a feedforward network structure , but possesses the properties of reservoir neural networks . The input weight matrix , set by a recurrent logistic mapping , forms the kernels that transform the input space to the higher-dimensional feature space .
This paper suggests an evolving hybrid neural fuzzy network ( eHFN ) modeling approach for realized volatility forecasting with jumps . The eHFN model is nonlinear , time-raying , and uses neurons based on uninorms and sigmoidal activation functions in a feedforward network topology . The approach simultaneously chooses the number of hidden layer neurons and corresponding neural networks weights .
The proposed model contains a convolutional network component to extract high-level features and a recurrent network component to enhance the modeling of the temporal characteristics of TS data . In addition , a feedforward fully connected network with the sparse group lasso ( SGL ) regularization is used to generate the final classification . The proposed architecture not only achieves satisfying classification accuracy , but also obtains good interpretability through the SGL regularization .
The main goal of this project is to apply differentiable and interactive t-norms to perform the conjunction and disjunction operations in a fuzzy neural network , to pattern classification and rule generation . The differentiable fuzzy neural network , denominated RNDD , object of this dissertation , is a feedforward fully-connected network with three layers : the input layer , the hidden layer of and neurons and the output layer of or neurons . It was used the backpropagation algorithm for training .
Finally , we present performance evaluation results and comparisons using both synthetic and real array data . In the case of feedforward architectures , the direction finding problem is formulated as a mapping problem , and we establish how a feedforward type network can be used to approximate the desired mapping . The network is trained on a representative set of input-output pairs .
Based on the analysis , it is found that the estimated annually electricity cost for HV Laboratory and EM Laboratory are RM 392.00 and RM 3197.76 respectively . For prediction of energy consumption of the two laboratories , Artificial Neural Network ( ANN ) algorithm is applied as computational tool using feedforward network type . The results show that the ANN is successfully modelled to predict the energy consumption .
The query encoder is implemented by another bidirectional GRU network . This time the last hidden state of the forward network is concatenated with the last hidden state of the backward network to form the query embedding , that is . The word embedding function is implemented in a usual way as a look - up table .
In this paper , we closely follow the neural translation model proposed in bahdanau2014neural and use a bidirectional recurrent neural network , which consists of two recurrent neural networks . The forward network reads the input sentence in a forward direction : where is a continuous embedding of the - th input symbol , and is a recurrent activation function . Similarly , the reverse network reads the sentence in a reverse direction ( right to left ) : At each location in the input sentence , we concatenate the hidden states from the forward and reverse RNNs to form a context set where .
Abstract We present a new network topology to avoid overfitting in two-layered feedforward networks . We use two additional linear layers and principal component analysis to reduce the dimension of both inputs and internal representations and to transmit the essential information . Thereby neurons with small variance in the output are removed , which results in better generalization properties .
Our method performs far better than many traditional artificial feature extraction methods with the help of standalone filter learning and multiscale local feature combination . Such structure cascaded by both linear layers with convolution filters and non-linear layers in binarization process shows better adaptability in different databases . With the help of parallel computing , training time is much shorter than PCANet and also more fixed compared to convolutional neural network .
In this paper , we propose three new methods to improve Matsui ’s branch-and-bound search algorithm which is known as the first generic algorithm for finding the best differential and linear trails . The three methods , named Reconstructing DDT and LAT According to Weight , Executing Linear Layer Operations in Minimal Cost and Merging Two 4-bit S-boxes into One 8-bit S-box respectively , can efficiently speed up the search process by reducing the search space as much as possible and reducing the cost of executing linear layer operations . We apply our improved algorithm to DESL and GIFT , which are still the hard instances for the automatic search methods .
This memory network is constructed to incorporate new information using a self-attention mechanism over the memory and input vectors to return a corresponding output vector for every timestep . Consequently , we obtain 3 output vectors which are then multiplied element-wisely into a single one ; and finally , we feed this vector to a linear neural layer to produce a scalar score for the triple . Experimental results show that our proposed R-MeN obtains state-of-the-art results on two well-known benchmark datasets WN11 and FB13 for triple classification task .
The parameters of the fuzzy layer of R-ELANFIS are randomly selected by incorporating the explicit knowledge representation using fuzzy membership functions . The parameters of the linear neural layer are determined by solving a constrained optimization problem in a regularized framework . Simulations on regression problems show that R-ELANFIS achieves similar or better generalization performance compared to well known kernel based regression methods and ELM based neuro-fuzzy systems .
We propose a novel feature based domain adaptation technique using hidden layer factorisation . This method is fundamentally different from existing methods because we use the domain features to calculate a linear combination of linear layers . These linear layers can capture domain specific information and information common to different domains .
The encoded low-level and high-level features are then combined by concatenation and convolution . Finally , a linear fully connected layer is used to evaluate the saliency of a queried region . A full resolution saliency map is obtained by querying the saliency of each local region of an image .
Time-delay and linear layer ( design ) intelligent computing expert system models were developed for predicting shelf life of soft mouth melting milk cakes stored at 6C . Linear layer ( design ) model gave best outcomes ( MSE : 0.000293366 , RMSE : 0.017127919 , R : 0.996479613 ) . Regression equations were developed based on these outcomes , shelf life was predicted 49.54 days .
In this paper , we propose a four stage method engineering process which aims at estimating impact of change . The process model described is a linear layered model . Impact sets are computed by analyzing dependency tractability relations with other connected method components .
In this paper , the neural network composed of a chaotic neuron layer and a linear neuron layer is used to construct a block cipher that transforms the data from the plaintext form into the unintelligible form under the control of the key . Among them , the chaotic neuron layer realizes data diffusion , the linear neuron layer realizes data confusion , and the two layers are repeated for several times to strengthen the cipher . The decryption process is symmetric to the encryption process .
Unfortunately , least squares estimation does not give a maximum likelihood ( ML ) estimate of the weights in the network . Logistic regression , on the other hand , gives ML estimates for single layer linear models only . This report describes how to obtain ML estimates of the weights in a multi-layer model , and compares LS-BP to ML-BP using several examples .
Link prediction is a task of inferring missing facts based on existing ones . We propose TuckER , a relatively simple but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples . TuckER outperforms all previous state - of - the - art models across standard link prediction datasets .
For web - scale recommender systems ( RSs ) , the input features are usually sparse , categorical - continuous - mixed , and high - dimensional . Linear models , such as logistic regression with FTRL , are widely adopted as they are easy to manage , maintain , and deploy . Because linear models lack the ability of learning feature interactions , data scientists have to spend a lot of work on engineering cross features in order to achieve better performance .
Models that are easy to optimize are easy to perturb . Linear models lack the capacity to resist adversarial perturbation ; only structures with a hidden layer ( where the universal approximator theorem applies ) should be trained to resist adversarial perturbation . RBF networks are resistant to adversarial examples .
In the meta - BiLSTM model , we concatenate the output , for each word , of its context sensitive character and word - based encodings , and put this through another BiLSTM to create an additional combined context sensitive encoding . This is followed by a final MLP whose output is passed to a linear layer for tag prediction . With this setup , each of the models can be optimized independently which we describe in more detail in § 3.5 .
For all experiments , we trained both seq2seq and BSO models with mini - batch Adagrad ( using batches of size 64 ) , and we renormalized all gradients so they did not exceed 5 before updating parameters . We did not extensively tune learning - rates , but we found initial rates of 0.02 for the encoder and decoder LSTMs , and a rate of 0.1 or 0.2 for the final linear layer ( i.e. , the layer tasked with making word - predictions at each time - step ) to work well across all the tasks we considered . Code implementing the experiments described below can be found at .
The translation model computes a distribution over the V possible target words y i + 1 by transforming the LSTM output h i via a linear layer with weights W o and bias
Thus , a reasonable compromise was to add a small correction factor which is learned per word , such that : where is a matrix projecting a low - dimensional embedding vector back up to the dimensionality of the projected LSTM hidden state of . This amounts to adding a bottleneck linear layer , and brings the CNN Softmax much closer to our best result , as can be seen in Table [ reference ] , where adding a 128 - dim correction halves the gap between regular and the CNN Softmax . Aside from a big reduction in the number of parameters and incorporating morphological knowledge from words , the other benefit of this approach is that out - of - vocabulary ( OOV ) words can easily be scored .
All these layers are separated by Rectifier Linear Units ( ReLu ) . Finally , a fully - connected linear layer projects to the output of the network , i.e. , the Q - values . The optimization employed to train the network is RMSProp ( with momentum parameter ) .
use this - localized convolution to define a convolutional neural network on graphs . subsection : Layer - Wise Linear Model A neural network model based on graph convolutions can therefore be built by stacking multiple convolutional layers of the form of Eq .
Note that all of these results are still lower than BM25 , which shows that they are not good at learning from weak supervision signals for ranking . We have also examined the score model with a network with a single linear hidden layer , with the embedding vector representation , which is equivalent to a linear regression model with the ability of representation learning . Comparing the results of this experiment with Score - Embed in Table [ reference ] , we can see that with a single - linear network we are not able to achieve a performance that is as good as a deep neural network with non - linearity .
— see for recent reviews of the different contexts and applications . The network architecture in the classification stage is that of a three layer neural network comprised from an input layer , a hidden layer of nonlinear units , and a linear output layer . The input weights are randomly chosen and untrained , and the output weights are trained in a single batch using least squares regression .
FWN is consisted with a Weight Layer and a nonlinear transformation , which decides the importance of each dimension in the part feature vector . Considering that a single linear Weight Layer might cause excessive response on some specific dimensions of the part vector , we add a nonlinear function to equalize the response of part feature vector , and the fused feature representation is where the and the are the global and part feature vectors . The and in Eq .
First , the network structures are substantially different . Rainbow uses noisy linear layers and ReLU activations throughout the network , whereas Reactor uses standard linear layers and concatenated ReLU activations throughout . To overcome partial observability , Rainbow , inheriting this choice from DQN , uses frame stacking .
Particle with ability of local search swarm optimization: PALSO for training of feedforward neural networks Feedback Regulation and Its Efficiency in Biochemical Networks Dynamic modeling of flexible-link manipulators using neural networks with application to the SSRMS Analysis of Neural Networks with Redundancy Automatic classification of RDoC positive valence severity with a neural network. Process Modelling Using Artificial Neural Networks On Generalization by Neural Networks Handbook of neural computing applications xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems Dynamic Integration of Background Knowledge in Neural NLU Systems Non-Autoregressive Neural Machine Translation A Decomposable Attention Model for Natural Language Inference Globally Normalized Transition-Based Neural Networks Speech recognition with deep recurrent neural networks Variational Autoencoders for Collaborative Filtering Calibrating Energy-based Generative Adversarial Networks BOOM: Broadcast Optimizations for On-chip Meshes One kind of the boom structure and connection reinforcement construction method for continuous box girder bridges psc An alternative boom design and welding technique to minimize energy consumption during boom production The Boom Hierarchy Functional Programming, Glasgow 1993 Structure of burner boom An Advanced VOF Algorithm for Oil Boom Design Performance Validation of the Triangular Rollable and Collapsible Mast Hydraulic concrete boom-type full automatic feeding system was stirred for one cloth feed Attitude control design for flying boom during air-to-air refuelling Don't Try This at Home: No-Go Theorems for Distributive Laws Stochastic resonance in feedforward acupuncture networks Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map Evolving hybrid neural fuzzy network for realized volatility forecasting with jumps GCRNN: Group-Constrained Convolutional Recurrent Neural Network Rede Neural Difusa com T-normas Diferenciáveis e Interativas Direction finding with artificial neural networks Artificial neural network based technique for energy management prediction Text Understanding with the Attention Sum Reader Network A Character-level Decoder without Explicit Segmentation for Neural Machine Translation Two Strategies to Avoid Overfitting in Feedforward Networks Face Recognition Using Local PCA Filters Improving Matsui's Search Algorithm for the Best Differential/Linear Trails and its Applications for DES, DESL and GIFT Relational Memory-based Knowledge Graph Embedding Regularized extreme learning adaptive neuro-fuzzy algorithm for regression and classification Feature Based Domain Adaptation for Neural Network Language Models with Factorised Hidden Layers ELD-Net: An Efficient Deep Learning Architecture for Accurate Saliency Detection Development of Intelligent Computing Expert System Models for Shelf Life Prediction of Soft Mouth Melting Milk Cakes Dependency based Process Model for Impact Analysis: A Requirement Engineering Perspective A block cipher based on chaotic neural networks Maximum likelihood training of connectionist models: comparison with least squares back-propagation and logistic regression. TuckER: Tensor Factorization for Knowledge Graph Completion xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems Explaining and Harnessing Adversarial Examples Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings Sequence-to-Sequence Learning as Beam-Search Optimization A Convolutional Encoder Model for Neural Machine Translation Exploring the Limits of Language Modeling Deep Reinforcement Learning with Double Q-Learning Semi-Supervised Classification with Graph Convolutional Networks Neural Ranking Models with Weak Supervision Enhanced image classification with a fast-learning shallow convolutional neural network Pose-Driven Deep Convolutional Model for Person Re-identification The Reactor: A Sample-Efficient Actor-Critic Architecture
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IJCNN IROS KDD ICLR EMNLP ACL 2013 IEEE International Conference on Acoustics, Speech and Signal Processing WWW ICLR Functional Programming CIFEr ACL ACL CCBR EMNLP/IJCNLP KDD ICLR ACL EMNLP ACL ArXiv AAAI ICLR SIGIR 2015 International Joint Conference on Neural Networks (IJCNN) 2017 IEEE International Conference on Computer Vision (ICCV) ArXiv
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