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Exponential Stabilization of Time‐varying Delayed Complex‐valued Memristor‐based Neural Networks Via Impulsive Control
Author(s) -
Li Xiaofan,
Fang Jianan,
Li Huiyuan,
Duan Wenyong
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.1729
Subject(s) - memristor , control theory (sociology) , artificial neural network , exponential stability , exponential function , control (management) , linear matrix inequality , computer science , mathematics , matrix (chemical analysis) , mathematical optimization , nonlinear system , engineering , artificial intelligence , physics , mathematical analysis , electronic engineering , materials science , quantum mechanics , composite material
The exponential stabilization problem for a class of time‐varying delayed complex‐valued memristor‐based neural networks via discontinuous impulsive control is investigated in this paper. Firstly, the time‐varying delayed complex‐valued memristor‐based neural networks is translated to real‐valued memristor‐based neural networks. Secondly, an impulsive control law is constructed to guarantee exponential stabilization of the complex‐valued memristor‐based neural networks with time‐varying delays. Thirdly, by constructing an appropriate Lyapunov Krasovskii functional and adopting linear matrix inequality (LMI) technique, some sufficient exponential stabilization criteria are derived. Finally, an illustrative example is provided to illustrate the effectiveness of the obtained theoretical results.