Global Exponential Stability of MAM Neural Network with Time-Varying Delays

Author

Wang, Ming ; Zhou, Tiejun ; Fang, Haiquan

Author_Institution

Coll. of Orient Sci. & Technol., Hunan Agric. Univ., Changsha, China

fYear

2010

fDate

10-12 Dec. 2010

Firstpage

1

Lastpage

4

Abstract

A mathematical model of multidirectional associative memory(MAM) neural networks with varying time delays is proposed. By using Brouwer fixed point theorem, a sufficient condition for the existence of an equilibrium point is obtained. And by constructing a suitable Lyapunov function, a sufficient condition for the global exponential stability of an equilibrium point is obtained, which depends on delays. The results are new for multidirectional associative memory neural networks. An example and its numerical simulation are given to illustrate the effectiveness of the obtained results.

Keywords

Lyapunov methods; delays; neurocontrollers; time-varying systems; Brouwer fixed point theorem; MAM neural network; global exponential stability; multidirectional associative memory; suitable Lyapunov function; time-varying delays; Artificial neural networks; Associative memory; Biological neural networks; Delay; Neurons; Numerical stability; Stability analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering (CiSE), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5391-7

Electronic_ISBN

978-1-4244-5392-4

Type

conf

DOI

10.1109/CISE.2010.5676984

Filename

5676984