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Training trajectories by continuous recurrent multilayer networks

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4 Author(s)
Leistritz, L. ; Inst. of Med. Statistics, Comput. Sci. & Documentation, Friedrich-Schiller-Univ., Jena, Germany ; Galicki, M. ; Witte, H. ; Kochs, E.

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

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Neural Networks, IEEE Transactions on  (Volume:13 ,  Issue: 2 )