Abstract

. We introduce a type of fully connected Recurrent Neural Networks (RNN) with special mathematical features which allows us to determine its qualitative dynamical behaviour. Using these properties we describe a learning framework for the generation of sequences to be applied to nonlinear control problems. The potential of this approach is demonstrated by applying the learning framework to the adaptive leg control of the six-legged walking machine LAURON II. 1. Introduction During the last years the application of RNNs gained constantly growing attention. The first attempt to formulate a learning algorithm for fully connected RNNs with deterministic discrete time dynamics (BPTT) dates back to one of the original publications of the backpropagation algorithm [12]. For practical applications convergence of this algorithm proved to be too slow and could not be guaranteed. So one favorite starting point to handle the classification or generation of time series was the application of ...

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