Non-fragile mixed H∞ and passive asynchronous state estimation for Markov jump neural networks with randomly occurring uncertainties and sensor nonlinearity
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- 作者：Shicheng HuoAuthor Vitae ; Mengshen ChenAuthor Vitae ; Hao Shen ; haoshen10@gmail.comAuthor Vitae
- 关键词：Markov jump neural networks ; Randomly occurring uncertainties ; Mixed H&infin ; and passive asynchronous state estimation ; Sensor nonlinearity
- 刊名：Neurocomputing
- 出版年：2017
- 出版时间：1 March 2017
- 年：2017
- 卷：227
- 期：Complete
- 页码：46-53
- 全文大小：609 K
- 卷排序：227
文摘
This paper is concerned with the non-fragile mixed H∞H∞ and passive asynchronous state estimation problem for uncertain discrete-time Markov jump neural networks (MJNNs). Both the uncertainties of system and the sensor nonlinearity are considered to be randomly occurring which are governed by a set of Bernoulli distributed white sequences. Since inaccuracies or uncertainties may occur in the designed state estimator and the complete mode synchronization between plant and state estimator is hardly possible, a non-fragile asynchronous state estimator design method is presented. By using an optimize matrix decoupling approach and Lyapunov-Krasovskii methodology, some sufficient conditions for the existence of non-fragile mixed H∞H∞ and passive asynchronous state estimator are proposed. A numerical example is presented to demonstrate the effectiveness of our proposed method.