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Stability analysis for switched Hopfield neural networks with time delay
Author(s) -
Lian Jie,
Zhang Kai,
Feng Zhi
Publication year - 2011
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.1005
Subject(s) - hopfield network , artificial neural network , control theory (sociology) , stability (learning theory) , lyapunov function , exponential stability , computer science , state (computer science) , function (biology) , matrix (chemical analysis) , signal (programming language) , hysteresis , mathematics , algorithm , nonlinear system , artificial intelligence , control (management) , physics , materials science , quantum mechanics , machine learning , evolutionary biology , composite material , biology , programming language
SUMMARY This paper considers the asymptotic stability problem for switched Hopfield neural networks with time‐varying delay under the hysteretic switching rule. The single and multiple Lyapunov function methods are employed to design the hysteretic switching rule dependent on the current state and previous value of the switching signal. Sufficient conditions are given in terms of linear matrix inequalities to guarantee the stability of such a system. Two examples illustrate the effectiveness of the proposed approaches. Copyright © 2011 John Wiley & Sons, Ltd.