Dynamic Neural Network for Prediction and Identification of Nonlinear Dynamic Systems

Abstract

An attempt has been made to establish a nonlinear dynamic discrete-time neuron model, the so called Dynamic Elementary Processor (DEP). This dynamic neuron disposes of local memory, in that it has dynamic states. Based on the DEP neuron, a Dynamic Multi Layer Perceptron Neural Network is proposed to predict a time series of nonlinear chaotic system. As an another application of the proposed Dynamic Neural Network (DNN), the identification of a dynamic discrete-time nonlinear system whose measurement data are spoiled with noise is performed. To accelerate the convergence of proposed extended dynamic error back propagation learning algorithm, the momentum method is applied. The learning results are presented in terms that are insensitive to the learning data range and allow easy comparison with other learning algorithms, independent of machine architecture or simulator implementation.