Title :
Modeling nonlinear systems with cellular neural networks
Author :
Puffer, F. ; Tetzlaff, R. ; Wolf, D.
Author_Institution :
Inst. fur Angewandte Phys., Frankfurt Univ., Germany
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;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3192-3
DOI :
10.1109/ICASSP.1996.550786
