Intelligent optimal control with dynamic neural networks
Neural Networks
Modeling and prediction with a class of time delay dynamic neural networks
Applied Soft Computing
Time delay dynamic fuzzy networks for time series prediction
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
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This paper addresses the problem of training trajectories by means of continuous recurrent neural networks whose feedforward parts are multilayer perceptrons. Such networks can approximate a general nonlinear dynamic system with arbitrary accuracy. The learning process is transformed into an optimal control framework where the weights are the controls to be determined. A training algorithm based upon a variational formulation of Pontryagin's maximum principle is proposed for such networks. Computer examples demonstrating the efficiency of the given approach are also presented