Robust Stability Analysis for Interval Cohen–Grossberg Neural Networks With Unknown Time-Varying Delays

Author

Zhang, Huaguang ; Wang, Zhanshan ; Liu, Derong

Author_Institution

Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang

Volume

19

Issue

11

fYear

2008

Firstpage

1942

Lastpage

1955

Abstract

In this paper, robust stability problems for interval Cohen-Grossberg neural networks with unknown time-varying delays are investigated. Using linear matrix inequality, M -matrix theory, and Halanay inequality techniques, new sufficient conditions independent of time-varying delays are derived to guarantee the uniqueness and the global robust stability of the equilibrium point of interval Cohen-Grossberg neural networks with time-varying delays. All these results have no restriction on the rate of change of the time-varying delays. Compared to some existing results, these new criteria are less conservative and are more convenient to check. Two numerical examples are used to show the effectiveness of the present results.

Keywords

delays; linear matrix inequalities; neural nets; stability; time-varying systems; Halanay inequality techniques; M-matrix theory; interval Cohen-Grossberg neural networks; linear matrix inequality; robust stability analysis; time-varying delays; Associative memory; Biological system modeling; Biological systems; Evolution (biology); Hopfield neural networks; Linear matrix inequalities; Neural networks; Robust stability; Sufficient conditions; Uncertainty; $M$-matrix; Cohen–Grossberg neural networks; Halanay inequality; interval neural networks; linear matrix inequality (LMI); robust stability; time-varying delays; Algorithms; Artificial Intelligence; Computer Simulation; Feedback; Models, Statistical; Pattern Recognition, Automated; Time Factors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2008.2006337

Filename

4666771