Exponential stability on stochastic neural networks with discrete interval and distributed delays

Authors:
Rongni Yang;Zexu Zhang;Peng Shi
Affiliations:
Space Control and Inertial Technology Research Center, Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, China and Faculty of Advanced Technology, University o ...;School of Astronautics, Harbin Institute of Technology, Harbin, China;Faculty of Advanced Technology, University of Glamorgan, Pontypridd, UK and School of Engineering and Science, Victoria University, Melbourne, Vic., Australia
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

This brief addresses the stability analysis problem for stochastic neural networks (SNNs) with discrete interval and distributed time-varying delays. The interval time-varying delay is assumed to satisfy 0 d1 ≤ d(t) ≤ d2 and is described as d(t) = d1 + h(t) with o ≤ h(t) ≤ d2 - d1. Based on the idea of partitioning the lower bound d1, new delay-dependent stability criteria are presented by constructing a novel Lyapunov-Krasovskii functional, which can guarantee the new stability conditions to be less conservative than those in the literature. The obtained results are formulated in the form of linear matrix inequalities (LMIs). Numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results.