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In this paper, novel theorem and corollaries are presented regarding the robust exponential stability of uncertain neural networks with mixed time-varying delays including discrete delays and distributed delays. The stability conditions in the new results improve and generalize existing ones. Several examples are included to show the effectiveness of the result.
In this paper, we considers the global robust exponential stability of uncertain neural networks with discontinuous activation functions and time-varying delays. Based on the Lyapunov-Krasovskii stability theory, we originally analyze the global robust exponential stability of discontinuous neural networks with time-varying delays using the linear matrix inequalities (LMIs). Thus, our results are...
This paper is concerned with the problem of robust exponential stability for discrete-time BAM neural networks with mode-dependent time delays and Markovian jump parameters, by utilizing the Lyapunov functional and combining with the linear matrix inequality (LMI) approach, the global exponential stability is investigated. The time delay varies in an interval and depends on the mode of operation....
In this paper, a new criterion is established for global robust asymptotic stability of a class of interval neural networks with multiple constant delays via the Lyapunov-Krasovskii stability theory and the linear matrix inequality (LMI) approach. A numerical example is also given to show the effectiveness of our results.
This paper deals with the problem of exponential stability for a class of discrete-time recurrent neural networks with time-varying delay by employing an improved free-weighting matrix approach. The relationship among the time-varying delay, its upper bound and their difference is taken into account. As a result, a new and less conservative delay-dependent stability criterion is obtained without ignoring...
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