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Finite‐time synchronization for memristor‐based BAM neural networks with stochastic perturbations and time‐varying delays
Author(s) -
Zhang Yanping,
Li Lixiang,
Peng Haipeng,
Xiao Jinghua,
Yang Yixian,
Zheng Mingwen,
Zhao Hui
Publication year - 2018
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4302
Subject(s) - memristor , synchronization (alternating current) , control theory (sociology) , settling time , computer science , bidirectional associative memory , artificial neural network , nonlinear system , content addressable memory , control (management) , control engineering , engineering , artificial intelligence , step response , channel (broadcasting) , computer network , physics , quantum mechanics , electrical engineering
Summary This paper focuses on the finite‐time synchronization issue of drive‐response memristor‐based bidirectional associative memory neural networks (MBAMNNs) with stochastic perturbations and time‐varying delays. Based on the mathematical model of memristor, definition of finite‐time stability for stochastic differential system and the drive‐response concept, some novel sufficient conditions are given to ensure the finite‐time synchronization of drive‐response MBAMNNs with stochastic perturbations and time‐varying delays. We design novel nonlinear feedback controllers to control the synchronization error to converge zero in a finite time, and the settling time for synchronization can be easily obtained. In addition, as two special cases, the finite‐time synchronization control criteria for bidirectional associative memory neural networks with stochastic perturbations and time‐varying delays and MBAMNNs without stochastic perturbations are also given. Finally, two numerical simulations are showed to demonstrate the correctness of main results.