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In this paper, we consider the problem of sampled-data state estimation of Markovian jump delayed static neural networks. By constructing a suitable Lyapunov–Krasovskii functional with double and triple integral terms and using Jensen inequality, delay-dependent criteria are presented so that the error system is asymptotically stable. Instead of the continuous measurement, the sampled measurement is employed to estimate the neuron states. It is further demonstrated that the configuration of the gain matrix of state estimator is changed to find a feasible solution of a linear matrix inequalities, which is efficiently facilitated by available algorithms. Finally, two numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

The motivation behind this paper is to explore the issue of passivity and passification for delayed neural networks with Markov jump parameters. A state feedback control approach with non-uniform sampling period is considered. The interest of this paper lies in the thought about another basic uniqueness wound up being less conservatism than watched Jensen’s inequality and takes totally the relationship between the terms in the Leibniz-Newton formula inside the arrangement of linear matrix inequalities. By utilizing the Lyapunov-Krasovskii functional strategy, a novel delay-dependent passivity criterion is developed with respect to linear matrix inequalities to guarantee the Markov jump delayed neural frameworks to be passive. Passivity and passification problems are tackled by using mode-dependent non-uniform sampled-data control. Using many examples from the literature, it is exhibited that the proposed stabilization theorem is less direct than past results. Finally, the framework is associated with benchmark issue, exhibiting to gather compelling stability criteria for reasonable issues, using the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, we consider the issue of delay-dependent H∞ performance state estimation of static delayed neural networks using sampled-data control. A sensible Lyapunov-Krasovskii functional with triple and quadruplex integral terms is constructed. By using Jensen’s inequality, Wirtinger-based inequality, and reciprocally convex technique, the stability conditions are derived. Delay-dependent criterion is acquired under which the estimation error framework is asymptotically stable with an endorsed H∞ performance. Instead of the continuous measurement, the sampled measurement is employed to estimate the neuron states. It is further demonstrated that the configuration of the gain matrix of state estimator is changed to find a feasible solution of a linear matrix inequalities, which is efficiently facilitated by available algorithms. At last, numerical cases are incorporated to demonstrate that the proposed technique is less moderate than existing ones. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of dissipativity analysis for discrete-time Markovian jumping neural networks with time-varying delays. Based on an appropriate Lyapunov-Krasovskii functional and by using the latest free-weighting matrix method, a sufficient condition is established to ensure that the neural networks under consideration is strictly (, , )- -dissipative. The derived conditions are presented in terms of linear matrix inequalities. A numerical example is presented to illustrate the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In this paper, we are concerned with the problem of state estimation of Takagi–Sugeno (T–S) fuzzy delayed neural networks with Markovian jumping parameters via sampled-data control. Based on the fuzzy-model-based control approach and linear matrix inequality (LMI) technique, several novel conditions are derived to guarantee the stability of the suggested system. A new class of Lyapunov functional, which contains integral terms, is constructed to derive delay-dependent stability criteria. Some characteristics of the sampling input delay are proposed based on the input delay approach. Numerical examples are given to illustrate the usefulness and effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]