Global Robust Stability Analysis Interval Bidirectional Associative Memory Neural Networks with Inverse Lipschitz Neuron Activations

Author

Gu, Yaning ; Liu, Deyou ; Zhang, Jingwen ; Wu, Wenjuan

Author_Institution

Dept. of Sci., Univ. of Yanshan, Qin Huangdao, China

fYear

2010

fDate

23-24 Oct. 2010

Firstpage

349

Lastpage

353

Abstract

In the paper, by using topological degree theory and Lyapunov function method, the issue of global robust stability is investigated for a class of interval bidirectional associative memory neural networks with inverse Lipschitz neuron activations, a novel sufficient conditions are established towards the existence, uniqueness and global robust stability of the equilibrium point, finally, a examples with their simulations are given to show the effectiveness of the theoretical results.

Keywords

Lyapunov methods; content-addressable storage; neural nets; topology; Lyapunov function method; interval bidirectional associative memory neural networks; inverse Lipschitz neuron activation; robust stability analysis; topological degree theory; Artificial neural networks; Associative memory; Linear matrix inequalities; Mathematical model; Neurons; Robust stability; Stability analysis; global robust stability; inverse Lipschitz activations; topological degree theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Cryptography and Network Security, Data Mining and Knowledge Discovery, E-Commerce & Its Applications and Embedded Systems (CDEE), 2010 First ACIS International Symposium on

Conference_Location

Qinhuangdao

Print_ISBN

978-1-4244-9595-5

Type

conf

DOI

10.1109/CDEE.2010.72

Filename

5759349