OPTIMAL NN TOPOLOGY FOR TIME DEPENDENT PATTERNS Nicolae Szirbik, Dept. of Computer Science Technical University of Timisoara, Romania Abstract For a specific application, which is solved using the connectionist model, the first step is to choose the most appropriate topology. Trying to apply NNs in automated reasoning and robot command generation, we have proved some advantages of recurrent topologies. Their principal quality is the temporal dimension of functionality. Some difficulties with time dependent patterns (e.g. the dilatation of the patterns) are implicitly solved by RNNs. However, the gradient descent algorithms used to obtain a desired behavior of the RNN, (according with the patterns from the training base) have a complexity of O(pn^4), (p = number of patterns); under certain circumstances, it can be reduced to O(pn^3). Even worse, the needed number of epochs for training cannot be known in advance. A possible solution to overcome complexity is not to limit oneself to using RNNs. For a _SPECIFIC_ application (e.g. a class of robotics applications), we have used simple feedforward nets. There are some limitations and the most important is that patterns cannot be infinitely cyclic. But for limited time span patterns the approach is feasible. An artifice to "simulate" the temporal dimension is necessary. An interesting conclusion is that performance can be drastically improved by introducing a certain degree of recurrence. Applying this kind of recurrence does not affect the O(pn^2) complexity of the training algorithm. We tried also the unsupervised "winner-takes-all" strategy. The conclusion has been that these nets can be used as an input "interface" for the following feedforward net and with some original training rules we have obtained excellent results (using a K-winners strategy). Our work in this area has been applied to the development of a pilot input generator. The purpose of this application is to autonomously fly a plane into high G-number maneuvers. The NN has to generate the trajectories imposed for rudder, stick and throttle. One purpose of the talk is to discuss interesting aspects concerning the training process of the command generator.The solving manner of the pattern time dilatation and contraction will be presented. Experimental results using different NN architectures will be discussed (RNN, feedforward NN, "simple" recurrent NN, winner-takes-all NN). ---------------------------------------------------------------------