State estimation of T-S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control

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• 作者：M. Syed Ali^a ; ^{syedgru@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; N. Gunasekaran^a ; ^{gunasmaths@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Quanxin Zhu^b ; ^c ; ^{zqx22@126.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Lyapunov method ; Linear matrix inequality ; Sampled-data control ; T&ndash ; S fuzzy neural network ; Time-varying delay
• 刊名：Fuzzy Sets and Systems
• 出版年：2017
• 出版时间：1 January 2017
• 年：2017
• 卷：306
• 期：Complete
• 页码：87-104
• 全文大小：374 K

文摘

In this paper, we are concerned with the problem of state estimation of Takagi–Sugeno (T–S) fuzzy delayed neural networks with Markovian jumping parameters via sampled-data control. Based on the fuzzy-model-based control approach and linear matrix inequality (LMI) technique, several novel conditions are derived to guarantee the stability of the suggested system. A new class of Lyapunov functional, which contains integral terms, is constructed to derive delay-dependent stability criteria. Some characteristics of the sampling input delay are proposed based on the input delay approach. Numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results.