Stability Analysis of Markovian Jumping Stochastic Cohen–Grossberg Neural Networks With Mixed Time Delays

Author

Zhang, Huaguang ; Wang, Yingchun

Author_Institution

Northeastern Univ., Shenyang

Volume

19

Issue

2

fYear

2008

Firstpage

366

Lastpage

370

Abstract

In this letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free-weight matrix via Newton-Leibniz formula is required. Two numerical examples are included to show the effectiveness of the result.

Keywords

Markov processes; Newton method; asymptotic stability; delays; linear matrix inequalities; neural nets; Markovian jumping stochastic Cohen-Grossberg neural network; Matlab LMI toolbox; Newton-Leibniz formula; asymptotic stability analysis problem; discrete delay; distributed delay; free-weight matrix; linear matrix inequality; mixed time delay; system transformation; Cohen–Grossberg neural networks (CGNNs); Markovian jumping; delay-dependent criteria; linear matrix inequality (LMI); mixed delay; Computer Simulation; Markov Chains; Neural Networks (Computer); Nonlinear Dynamics; Time Factors;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2007.910738

Filename

4436183