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Exponential Synchronization of Memristive Chaotic Recurrent Neural Networks Via Alternate Output Feedback Control
Author(s) -
Li Xiaofan,
Fang Jianan,
Li Huiyuan
Publication year - 2018
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1562
Subject(s) - control theory (sociology) , synchronization (alternating current) , synchronization of chaos , artificial neural network , chaotic , computer science , exponential stability , lyapunov stability , controller (irrigation) , mathematics , control (management) , topology (electrical circuits) , artificial intelligence , nonlinear system , physics , combinatorics , quantum mechanics , agronomy , biology
This paper considers the global exponential synchronization problem of two memristive chaotic recurrent neural networks with time‐varying delays using periodically alternate output feedback control. First, the periodically alternate output feedback control rule is designed for the global exponential synchronization of two memristive chaotic recurrent neural networks. Then, according to the Lyapunov stability theory, we construct an appropriate Lyapunov‐Krasovskii functional to derive several new sufficient conditions guaranteeing exponential synchronization of two memristive chaotic recurrent neural networks under periodically alternate output feedback control. Compared with existing results on synchronization conditions on the basis of linear matrix inequalities of memristive chaotic recurrent neural networks, the derived results complement, extend earlier related results, and are also easy to validate in this paper. An illustrative example is provided to illustrate the effectiveness of the synchronization criteria.