Exponential state estimation for Markovian jumping neural networks with mixed time-varying delays and discontinuous activation functions

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• 作者：Huaiqin Wu ; Leifei Wang ; Yu Wang…
• 刊名：International Journal of Machine Learning and Cybernetics
• 出版年：2016
• 出版时间：August 2016
• 年：2016
• 卷：7
• 期：4
• 页码：641-652
• 全文大小：892 KB
• 刊物类别：Engineering
• 刊物主题：Artificial Intelligence and Robotics
  Statistical Physics, Dynamical Systems and Complexity
  Computational Intelligence
  Control , Robotics, Mechatronics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1868-808X
• 卷排序：7

文摘

This paper is concerned with the exponential state estimation issue for Markovian jumping neural networks with mixed time-varying delays and discontinuous activation functions. By introducing triple-integral terms and quadruple integrals term in Lyapunov–Krasovskii functional, the obtained Lyapunov matrices are distinct for different system modes. Based on the nonsmooth analysis theory and by applying stochastic analysis techniques, the full-order state estimator is designed to ensure that the corresponding error system is exponentially stable in mean square. The desired mode-dependent and delay-dependent estimator can be achieved by solving a set of linear matrix inequalities. Finally, two simulation examples are given to illustrate the validity of the theoretical results.KeywordsNeural networksState estimationDiscontinuous neuron activationsMarkovian jumping parametersMixed time-varying delays