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Global projective lag synchronization of fractional order memristor based BAM neural networks with mixed time varying delays
Author(s) -
Pratap Anbalagan,
Raja Ramachandran,
Sowmiya Chandran,
Bagdasar Ovidiu,
Cao Jinde,
Rajchakit Grienggrai
Publication year - 2020
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.2075
Subject(s) - memristor , differential inclusion , lemma (botany) , control theory (sociology) , synchronization (alternating current) , mathematics , artificial neural network , controller (irrigation) , fractional calculus , topology (electrical circuits) , computer science , control (management) , mathematical optimization , artificial intelligence , ecology , agronomy , poaceae , combinatorics , electrical engineering , biology , engineering
This paper addresses Master–Slave synchronization for some memristor‐based fractional‐order BAM neural networks (MFBNNs) with mixed time varying delays and switching jumps mismatch. Firstly, considering the inherent characteristic of FMNNs, a new type of fractional‐order differential inequality is proposed. Secondly, an adaptive switching control scheme is designed to realize the global projective lag synchronization goal of MFBNNs in the sense of Riemann‐Liouville derivative. Then, based on a suitable Lyapunov method, under the framework of set‐valued map, differential inclusions theory, fractional Barbalat's lemma and proposed control scheme, some new projective lag synchronization criteria for such MFBNNs are obtained. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed theoretical analysis.