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

被引：0
作者：
Huaiqin Wu
Leifei Wang
Yu Wang
Peifeng Niu
Bolin Fang
机构：
[1] Yanshan University,School of Science
[2] Yanshan University,School of Electrical Engineering
[3] Yanshan University,School of Information Science and Engineering
来源：
International Journal of Machine Learning and Cybernetics | 2016年 / 7卷
关键词：
Neural networks; State estimation; Discontinuous neuron activations; Markovian jumping parameters; Mixed time-varying delays;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：641 / 652
页数：11
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