文章基本信息

标题：Exponential passivity conditions on neutral stochastic neural networks with leakage delay and partially unknown transition probabilities in Markovian jump
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作者：Tao Wu ; Jinde Cao ; Lianglin Xiong 等
期刊名称：Advances in Difference Equations
印刷版ISSN：1687-1839
电子版ISSN：1687-1847
出版年度：2018
卷号：2018
期号：1
页码：317
DOI：10.1186/s13662-018-1732-6
语种：English
出版社：Hindawi Publishing Corporation
摘要：This paper studies the problem of exponential passivity for neutral stochastic neural networks (NSNN) with leakage delay and Markovian jump. The Markovian jump has partially unknown transition probabilities (PUTPs). By utilizing the Itô differential rule, choosing a suitable Lyapunov–Krasovskii functional and combining with the inequality technique, the sufficient delay-dependent exponential passivity criteria are obtained. These sufficient conditions are provided in the form of linear matrix inequalities (LMIs), which can be easily solved by LMI toolbox in Matlab. Finally, two simulated numerical examples are discussed in detail to illustrate the effectiveness of the established results.
关键词：Neutral stochastic neural networks ; Partially unknown transition probabilities ; Markovian jump ; Exponential passivity criteria ; Itô differential rule