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Modeling nonlinear systems with cellular neural networks

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3 Author(s)
Puffer, F. ; Inst. fur Angewandte Phys., Frankfurt Univ., Germany ; Tetzlaff, R. ; Wolf, D.

A learning procedure for the dynamics of cellular neural networks (CNN) with nonlinear cell interactions is presented. It is applied in order to find the parameters of CNN that model the dynamics of certain nonlinear systems, which are characterized by partial differential equations (PDEs). Values of a solution of the considered PDEs for a particular initial condition are taken as the training pattern at only a small number of points in time. Our results demonstrate that CNN obtained with our method approximate the dynamical behaviour of various nonlinear systems accurately. Results for two nonlinear PDEs, the Φ 4-equation and the sine-Gordon equation, are discussed in detail

Published in:

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on  (Volume:6 )

Date of Conference:

7-10 May 1996

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