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Exploring the nonlinear dynamic behavior of artificial neural networks

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2 Author(s)
Von Zuben, F.J. ; Sch. of Electr. Eng., State Univ. of Campinas, Brazil ; de Andrade Netto, M.L.

This paper explores the universal approximation capability exhibited by neural networks in the development of suitable architectures and associated training processes for nonlinear discrete-time dynamic system representation. The resulting architectures include recurrent and non recurrent multilayer neural networks and the derived training processes can be seen as optimization problems. Particular attention is given to the investigation of the dynamic behavior of a recurrent processing unit

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:2 )

Date of Conference:

27 Jun-2 Jul 1994