Stability Analysis for Discrete-Time Stochastic Neural Networks with Mixed Time Delays via Delay-Paritioning

Abstract:

This paper revisits the problem of stability analysis for discrete-time stochastic neural networks (DSNNs) with mixed time-varying delays in the state. Here the mixed time delays are assumed to be discrete and distributed time delays and the uncertainties are assumed to be time varying norm bounded parameter uncertainties. A new delay-dependent stability criterion is presented by constructing a novel Lyapunov-Krasovskii functional and utilizing the delay partitioning idea and free-weighting matrix approach, Which is less conservative than the existing ones. This criterion can be developed in the frame of convex optimization problems and then solved via standard numerical software. These conditions are formulated in the forms of linear matrix inequalities, which feasibility can be easily checked by using Matlab LMI Toolbox.

• Periodical: Advanced Materials Research (Volumes 228-229)
• Pages: 464-470
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.228-229.464
• Online since: April 2011
• Authors: Xia Zhou, Shou Ming Zhong
• Keywords: Asymptotic Stability in Mean Square, Delay-Dependent, Delay-Plaritioning, Discrete-Time Stochastic Neural Network, Linear Matrix Inequality (LMI), Mixed Time Delay

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