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This paper is focused on the problem of delay-dependent stabilization for neural networks (NNs) with discrete and distributed time-varying delays. The main objective of this work is to design a H∞ control law to ensure the asymptotical stability of the closed-loop system. Besides, the less conservative stability criterion is derived in terms of linear matrix inequalities (LMIs) by constructing an...
This study is focused on the problem of asymptotic stability for a class of Lur'e dynamical system with mixed time-varying delays and nonlinearity. Firstly, by constructing a new appropriate Lyapunov-Krasovkii functional, less conservative delay-dependent stability criteria are derived by using some integral inequalities. Besides, by dividing the mixed delays into many a nonuniformly subintervals...
This paper is concerned with analysis problem for the stability of the a stochastic discrete-time neural networks (DNNs) with discrete time-varying delay. By used some novel analysis techniques, stability theory and Lyapunov -Krasovskii function, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally asymptotically stable in mean square...
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