This paper is concerned with the mean-square exponential input-to-state stability problem for a class of stochastic Cohen-Grossberg neural networks. Different from prior works, neutral terms and mixed delays are discussed in our system. By employing the Lyapunov-Krasovskii functional method, Itô formula, Dynkin formula, and stochastic analysis theory, we obtain some novel sufficient conditions to ensure that the addressed system is mean-square exponentially input-to-state stable. Moreover, two numerical examples and their simulations are given to illustrate the correctness of the theoretical results.
In this paper, we offer an approach about the dissipativity of neutral-type memristive neural networks (MNNs) with leakage, additive time, and distributed delays. By applying a suitable Lyapunov-Krasovskii functional (LKF), some integral inequality techniques, linear matrix inequalities (LMIs) and free-weighting matrix method, some new sufficient conditions are derived to ensure the dissipativity of the aforementioned MNNs. Furthermore, the global exponential attractive and positive invariant sets are also presented. Finally, a numerical simulation is given to illustrate the effectiveness of our results.
In this paper, the finite-time (FT) and fixed-time synchronization (F-TS) problems of fuzzy inertial cellular neural networks (FICNNs) with mixed delays are discussed based on the Filippov solution theory and FT stability theory. Compared with related research, this paper considers the fuzzy inertial system combined with discontinuous activation of cellular neural networks (CNNs) for the first time. Then, by choosing appropriate variable transformation, the original system can be rewritten as a first-order differential system. Some novel and useful sufficient conditions for finite/fixed-time synchronization of FICNNs are established by applying three suitable discontinuous state feedback controllers. Finally, three numerical simulations are presented to elaborate the validity of the proposed methods.
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