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Mean Square Exponential Stability of Stochastic Delayed Static Neural Networks with Markovian Switching

  • He Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

This paper is concerned with globally exponential stability in the mean square of stochastic static neural networks with Markovian switching and time delay. Firstly, the mathematical model of this kind of recurrent neural networks is established by taking information latching and noise disturbance into consideration. Then, a stability condition, which is dependent on both time delay and system mode, is presented in terms of linear matrix inequalities. Based on it, the maximum value of the exponential decay rate can be efficiently found by solving a convex optimization problem.

Keywords

Static neural networks stability time delay Markovian switching exponential decay rate convex optimization 

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© Springer International Publishing Switzerland 2015

<SimplePara><Emphasis Type="Bold">Open Access</Emphasis> This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. </SimplePara> <SimplePara>The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</SimplePara>

Authors and Affiliations

  1. 1.School of Electronics and Information EngineeringSoochow UniversitySuzhouP. R. China

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