Exponential H ↿/sub> filtering analysis for discrete-time switched neural networks with random delays using sojourn probabilities

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• 作者：JinDe Cao ; R. Rakkiyappan ; K. Maheswari…
• 关键词：switched neural networks ; average dwell time ; sojourn probability method ; exponential stability
• 刊名：SCIENCE CHINA Technological Sciences
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：59
• 期：3
• 页码：387-402
• 全文大小：496 KB
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• 作者单位：JinDe Cao (1)
  R. Rakkiyappan (2)
  K. Maheswari (2)
  A. Chandrasekar (2)
  
  1. Department of Mathematics, Southeast University, Nanjing, 210096, China
  2. Department of Mathematics, Bharathiar University, Coimbatore, Tamilnadu, 641 046, India
  
• 刊物类别：Engineering
• 刊物主题：Chinese Library of Science
  Engineering, general
  
• 出版者：Science China Press, co-published with Springer
• ISSN：1869-1900

文摘

This paper is concerned with the exponential H _∞ filtering problem for a class of discrete-time switched neural networks with random time-varying delays based on the sojourn-probability-dependent method. Using the average dwell time approach together with the piecewise Lyapunov function technique, sufficient conditions are proposed to guarantee the exponential stability for the switched neural networks with random time-varying delays which are characterized by introducing a Bernoulli stochastic variable. Based on the derived H _∞ performance analysis results, the H _∞ filter design is formulated in terms of Linear Matrix Inequalities (LMIs). Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design procedure.