Global Asymptotic Stability Criteria for Stochastic Neutral-Type Delayed Neural Networks

Abstract

Abstract: The problem of global asymptotic stability analysis for a class of stochastic neutral-type neural networks with variable delays was investigated. By constructing an appropriate Lyapunov functional combined with inequality technique，sufficient stability criteria dependent on the size of the time delay were obtained in terms of linear matrix inequality (LMI). A class of neutral-type networks considered in the paper consists of distributed delays, stochastic perturbation and activation functions which are assumed to be constants (positive, negative, and zero), so these proposed criteria are universal and representative. The proposed stability results are more universal and less conservative than the existing ones. Finally, two numerical examples were given to demonstrate the effectiveness of the proposed results.

Key words: global asymptotic stability, stochastic perturbation, neural networks, linear matrix inequality (LMI), variable delays

CLC Number:

TP183

LIU Guo-Quan-1, YANG Xian-Yi-1, CHAI Yi-1, FU Wei-1, FENG Wei-2, SHI Guang-1. Global Asymptotic Stability Criteria for Stochastic Neutral-Type Delayed Neural Networks[J]. Journal of Shanghai Jiaotong University, 2011, 45(08): 1151-1157.

References

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