ANFISAdaptive Network-Based Fuzzy Inference System
ANFISAssociazione Nazionale dei Formatori Insegnanti Supervisori (Italian: National Association of Teacher Educators Supervisors)
ANFISAdaptive Neuro-Fuzzy Inference System
ANFISAdaptive Network Fuzzy Inference System
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Early cross-correlation indicated similarity between the ANFIS predicted data and the original data of 82 percent, a very positive outcome which we expect to increase over time as we continue to add data and variables to the system,' commented Dr.
This study will evaluate the performance of various data mining techniques such as ANFIS, ANNs and the M5 tree model for estimating daily soil temperature at different depths in semi arid regions such as Iran.
ANFIS is one of the popular neuro-fuzzy methods that is the hybrid combination of artificial neural networks (ANNs) and is based on Takagi-Sugeno fuzzy inference system (FIS) [21-23].
Five models were proposed for modeling emotions from Arousal and Valence: a model to determine the strongest emotion that is felt by the person (called the Strongest Emotion model and based on a model used by Mandryk and Atkins to determine emotions while playing an interactive game), a Mamdani's model and a Takagi-Sugeno's model (both called a rule-based FIS because of the structure of their respective rules), which calculates the angle and the intensity of the emotion and two ANFIS using a grid partition and a fuzzy clustering technique.
Too much training data can make it impossible for the ANFIS algorithm to minimize the error function to an acceptable level, resulting in spiky outputs and overly sensitive OS/US indicators or overtraining.
In general terms, an ANFIS is an algorithm that automatically adjusts Sugeno type fuzzy logic membership functions with the aid of training data.
Well known ANFIS (adaptive neurofuzzy inference system) structure is used for solving cervical cancer recognition [27], for optimizing the chiller loading [28], and for distinguishing ESES (electrical status epilepticus) and normal EEG (electroencephalography) signals [29].
Jacobsen, "An empirical model for preliminary seismic response estimation of free-plan nominally symmetric buildings using ANFIS," Engineering Structures, vol.
For the comparison of these results the best method was chosen between ANFIS and ANN, and used for the application implemented with the MatLab GUIDE.
In particular, this study explores the role of ANFIS in conjunction with SSL in classifying unknown cases (those that were not pursued for debt collection) as either a good case (recoverable) or a bad case (unrecoverable).
Estimation of Saturation Percentage of Soil Using Multiple Regression, ANN, and ANFIS Techniques.