Title :
A learning algorithm for cellular neural networks (CNN) solving nonlinear partial differential equations
Author :
Puffer, F. ; Tetzlaff, R. ; Wolf, D.
Author_Institution :
Inst. fur Angewandte Phys., Frankfurt Univ., Germany
Abstract :
A learning procedure for CNN is presented and applied in order to find the parameters of networks approximating the dynamics of certain nonlinear systems which are characterized by partial differential equations (PDE). Our results show that - depending on the training pattern - solutions of various PDE can be approximated with high accuracy by a simple CNN structure. Results for two nonlinear PDE, Burgers´ equation and the Korteweg-de Vries equation, are discussed in detail
Keywords :
Korteweg-de Vries equation; cellular neural nets; learning (artificial intelligence); nonlinear differential equations; partial differential equations; Burgers´ equation; Korteweg-de Vries equation; cellular neural networks; learning algorithm; nonlinear partial differential equations; Artificial neural networks; Backpropagation; Cellular neural networks; Differential equations; Learning systems; Neurofeedback; Nonlinear equations; Nonlinear systems; Partial differential equations; Piecewise linear approximation;
Conference_Titel :
Signals, Systems, and Electronics, 1995. ISSSE '95, Proceedings., 1995 URSI International Symposium on
Conference_Location :
San Francisco
Print_ISBN :
0-7803-2516-8
DOI :
10.1109/ISSSE.1995.498041
