Stochastic stability of Markovian jumping Hopfield neural networks with constant and distributed delays

Authors:
Hongyang Liu;Lin Zhao;Zexu Zhang;Yan Ou
Affiliations:
Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang Province 150001, China;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang Province 150001, China;The School of Astronautics, Harbin Institute of Technology, Harbin, Heilongjiang Province 150001, China;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang Province 150001, China
Venue:
Neurocomputing
Year:
2009

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Abstract

This paper investigates the problem of stability analysis for Markovian jumping Hopfield neural networks (MJHNNs) with constant and distributed delays. Some new delay-dependent stochastic stability criteria are derived based on a novel Lyapunov-Krasovskii functional (LKF) approach. These new criteria based on the delay partitioning idea prove to be less conservative, since the conservatism could be notably reduced by thinning the delay partitioning. Numerical examples are provided to show the effectiveness and advantage of the proposed techniques.