Periodic Oscillation for Cohen-Grossberg-Type Bidirectional Associative Memory Neural Networks with Neutral Time-Varying Delays

Author

Bai, Chuanzhi

Author_Institution

Dept. of Math., Huaiyin Teachers´´ Coll., Huaiyin, China

Volume

2

fYear

2009

fDate

14-16 Aug. 2009

Firstpage

18

Lastpage

23

Abstract

In this paper, a model describing dynamics of Cohen-Grossberg-type bidirectional associative memory neural networks with neutral time-varying delays is investigated by using the continuation theorem of Mawhin´s coincidence degree theory and the properties of an M-matrix. Without assuming the continuous differentiability of time-varying delays, some sufficient conditions on the existence of the periodic solutions are obtained. The result of this paper is new and extent previously known result. Finally, an illustrative example is given to show the effectiveness of the obtained result.

Keywords

content-addressable storage; delays; matrix algebra; neural nets; time-varying systems; Cohen-Grossberg-type bidirectional associative memory neural networks; M-matrix; Mawhin coincidence degree theory; continuation theorem; neutral time-varying delays; periodic oscillation; Associative memory; Computer networks; Delay effects; Educational institutions; Hopfield neural networks; Mathematical model; Mathematics; Neural networks; Signal processing; Sufficient conditions;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation, 2009. ICNC '09. Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-0-7695-3736-8

Type

conf

DOI

10.1109/ICNC.2009.630

Filename

5364034