A learning algorithm for the dynamics of CNN with nonlinear templates. I. Discrete-time case

Author

Tetzlaff, Ronald ; Wolf, D.

Author_Institution

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

fYear

1996

Firstpage

461

Lastpage

466

Abstract

A learning algorithm for the dynamics of discrete-time cellular neural networks (DTCNN) with gradient-based nonlinear templates is presented. For modeling the dynamics of nonlinear spatio-temporal systems with DTCNN, the algorithm is applied to find the network parameters. Results for two different nonlinear discrete-time systems are discussed in detail

Keywords

cellular neural nets; discrete time systems; dynamics; learning (artificial intelligence); nonlinear systems; discrete-time cellular neural networks; dynamics; gradient-based nonlinear templates; learning algorithm; nonlinear discrete-time systems; nonlinear spatio-temporal systems; nonlinear templates; Cellular neural networks; Computer aided software engineering; Design methodology; Heuristic algorithms; Learning systems; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Partial differential equations; Performance analysis;

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.1996.566618

Filename

566618

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1606070