Cellular Neural Field and Its Convergence Analysis

Author

Jinn-Wen Wu ; Kuang-Yow Lian

Author_Institution

Dept. of Appl. Math., Chung Yuan Christian Univ., Chung-li

Volume

17

Issue

6

fYear

2006

Firstpage

1639

Lastpage

1641

Abstract

A new continuum model complementary to traditional cellular neural networks is introduced in this note. We consider a cellular neural field formed by infinitely many cellular neurons and modelled by an integrodifferential equation. Then, using LaSalle´s invariance principle on Banach space, we show that the field quantity will asymptotically converge to an equilibrium state under the condition that all equilibria of the system are isolated. From the practical sense, the convergence indicates the essential capability of retrieving message from original raw data

Keywords

asymptotic stability; cellular neural nets; convergence of numerical methods; integro-differential equations; Banach space; LaSalle invariance principle; asymptotic stability; cellular neural networks; convergence analysis; integrodifferential equation; Artificial neural networks; Associative memory; Biological neural networks; Cellular neural networks; Convergence; Information retrieval; Integrodifferential equations; Neurons; Pattern recognition; Stability analysis; Associative memory; cellular neural networks; stability analysis; Algorithms; Information Storage and Retrieval; Neural Networks (Computer); Pattern Recognition, Automated; Signal Processing, Computer-Assisted;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2006.881058

Filename

4012049