Modeling nonlinear systems with cellular neural networks

Author

Puffer, F. ; Tetzlaff, R. ; Wolf, D.

Author_Institution

Inst. fur Angewandte Phys., Frankfurt Univ., Germany

Volume

6

fYear

1996

fDate

7-10 May 1996

Firstpage

3513

Abstract

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

Keywords

cellular neural nets; learning (artificial intelligence); nonlinear dynamical systems; partial differential equations; sine-Gordon equation; Φ⁴-equation; cellular neural network; dynamics; learning procedure; nonlinear cell interactions; nonlinear systems; partial differential equations; sine-Gordon equation; training pattern; Artificial neural networks; Cellular neural networks; Differential equations; Lattices; Learning systems; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Partial differential equations; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on

Conference_Location

Atlanta, GA

ISSN

1520-6149

Print_ISBN

0-7803-3192-3

Type

conf

DOI

10.1109/ICASSP.1996.550786

Filename

550786