A learning algorithm for the dynamics of CNN with nonlinear templates. II. Continuous-time case

Author

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

Author_Institution

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

fYear

1996

Firstpage

467

Lastpage

472

Abstract

A gradient-based learning algorithm for the dynamics of continuous-time CNN with nonlinear templates is presented. It is applied in order to find the parameters of CNN that model the dynamics of certain multidimensional nonlinear systems, which are characterized by partial differential equations (PDE). The efficiency of the algorithm is compared to that of a non-gradient-based learning procedure we have previously developed. Results for modeling two systems, whose dynamics are determined by nonlinear Klein-Gordon-equations, are discussed in detail

Keywords

cellular neural nets; continuous time systems; dynamics; learning (artificial intelligence); multidimensional systems; nonlinear systems; partial differential equations; continuous-time CNN; dynamics; efficiency; gradient-based learning algorithm; multidimensional nonlinear systems; nongradient-based learning procedure; nonlinear Klein-Gordon-equations; nonlinear templates; partial differential equations; Cellular neural networks; Computer aided software engineering; Context modeling; Convergence; Heuristic algorithms; Multidimensional systems; Nonlinear equations; Nonlinear systems; Partial differential equations; Steady-state;

fLanguage

English

Publisher

ieee

Conference_Titel

Cellular Neural Networks and their Applications, 1996. CNNA-96. Proceedings., 1996 Fourth IEEE International Workshop on

Conference_Location

Seville

Print_ISBN

0-7803-3261-X

Type

conf

DOI

10.1109/CNNA.1996.566619

Filename

566619