Search

This paper concerns the problem ofstate estimation of discrete-time Markov jump neural networks with general transition probabilities and output quantization. In terms of a Markov chain, the event of mode switching at various times is considered in both the parameters and the discrete delays of the neural networks. The state estimation is analyzed when the information is transmitted over a digital communication channel. In this concern the design of the quantizer and the estimator is jointly investigated. The purpose of the concerned problem is to design a mode-dependent state estimator such that the network states are estimated through available output measurements such that the dynamics of the estimation error is stochastically stable. Novel Lyapunov–Krasovskii functional is constructed and sufficient constraints are derived in terms of linear matrix inequalities such that the existence of the desired estimator is assured. The effectiveness of the proposed approach is illustrated through a simulation example. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the stochastic sampled data robust stabilisation of T-S fuzzy neutral systems with randomly occurring uncertainties and time-varying delays. The sampling period is assumed to bemin number, whose occurrence probabilities are given constants and satisfy Bernoulli distribution. By introducing an improved Lyapunov–Krasovskii functional with new triple integral terms and by combining both the convex combination technique and reciprocal convex technique, delay-dependent robust stability criteria are obtained in terms of linear matrix inequalities. These linear matrix inequalities can be easily solved by using standard convex optimisation algorithms. The designed stochastic sampled data fuzzy controller gain can be obtained. Finally, three numerical examples are given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper is concerned with the global asymptotic stability of a class of stochastic bidirectional associative memory neural networks with both multiple discrete and distributed time-varying delays. A new criterion of asymptotic stability is derived in terms of linear matrix inequality, which can be efficiently solved by a standard numerical software. An illustrative numerical example is also given to show the applicability and effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

In this article, the global exponential stability of neutral-type bidirectional associative memory (BAM) neural networks with time-varying delays is analysed by utilizing the Lyapunov-Krasovskii functional and combining with the linear matrix inequality (LMI) approach. New sufficient conditions ensuring the global exponential stability of neutral-type BAM neural networks is obtained by using the powerful MATLAB LMI control toolbox. In addition, an example is provided to illustrate the applicability of the result. [ABSTRACT FROM AUTHOR]

In this paper, by utilizing the Lyapunov-Krasovkii functional and combining with the linear-matrix inequality (LMI) approach, we analyse the global exponential stability of delayed neural networks of neutral type. In addition, the examples are provided to illustrate the applicability of the result using LMI control toolbox in MATLAB. [ABSTRACT FROM AUTHOR]