期刊
NEURAL NETWORKS
卷 98, 期 -, 页码 192-202出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2017.11.007
关键词
Memristor; Stochastic; BAM neural networks; pth moment exponential stability
资金
- National Natural Science Foundation of China [61075087]
- Natural Science Foundation of Guangdong Province [2017A030313037, 2015A030310426, 2014A030310469]
- outstanding young teacher training plan of Guangdong Province [YQ2015118]
- Foundation for Distinguished Young Talents in Higher Education of Guangdong Province [2014KQNCX187, 2013LYM_0060]
- innovation strong school project of department of education of Guangdong province [20170504185]
- characteristic innovation project of universities of Guangdong province [2016GXJK117]
Stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays play an increasingly important role in the design and implementation of neural network systems. Under the framework of Filippov solutions, the issues of the pth moment exponential stability of stochastic memristor-based BAM neural networks are investigated. By using the stochastic stability theory, Ito's differential formula and Young inequality, the criteria are derived. Meanwhile, with Lyapunov approach and Cauchy-Schwarz inequality, we derive some sufficient conditions for the mean square exponential stability of the above systems. The obtained results improve and extend previous works on memristor-based or usual neural networks dynamical systems. Four numerical examples are provided to illustrate the effectiveness of the proposed results. (C) 2017 Elsevier Ltd. All rights reserved.
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