Global robust passivity analysis for stochastic fuzzy interval neural networks with time-varying delays

详细信息    查看全文

• 作者：P. Balasubramaniam ; ^{balugru@gmail.com} ; G. Nagamani
• 关键词：Linear matrix inequality (LMI) ; Lyapunov method ; Passivity ; Stochastic interval neural networks ; Time-varying delays
• 刊名：Expert Systems with Applications
• 出版年：2012
• 出版时间：January, 2012
• 年：2012
• 卷：39
• 期：1
• 页码：732-742
• 全文大小：398 K

文摘

In this paper, the problem of passivity analysis is investigated for uncertain stochastic fuzzy interval neural networks with time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. For the neural networks under study, a generalized activation function is considered, where the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique, new delay-dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs), which can be solved by some standard numerical packages. Finally, numerical examples are given to show the effectiveness and merits of the proposed method.