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M. Syed Ali,K. Meenakshi,N. Gunasekaran 제어·로봇·시스템학회 2017 International Journal of Control, Automation, and Vol.15 No.6
The problem of finite-time H∞ performance of discrete time neural network with norm-bounded disturbancesand time varying delays is studied in this paper. By constructing a delay-dependent Lyapunov-Krasovskiifunctional and using a two-term approximation of the time-varying delay, sufficient conditions of finite-time stabilityare derived and expressed in terms of linear matrix inequalities (LMIs). The derived stability conditions canbe applied into analyzing the finite-time stability and deriving the maximally tolerable delay. Comparision withthe existing results given to show that, the derived stability conditions are less conservative. Finally, numericalexamples are provided to illustrate the effectiveness of the proposed method.
Finite Time H∞ Boundedness of Discrete-time Markovian Jump Neural Networks with Time-varying Delays
M. Syed Ali,K. Meenakshi,N. Gunasekaran 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.1
This paper is concerned with the problem of finite-time H∞ boundedness of discrete-time Markovian jumping neural netwoks with time-varying delays. A new sufficient condition is presented which guarantees the stability of the closed-loop system and the same time maximizes the boundedness on the non-linearity. An extension of fixed transition probability Markovian model is combined to time-varying transition probabilities has offered. By constructing a novel Lyapunov - Krasovskii functional, the system under consideration is subject to interval timevarying delay and norm-bounded disturbances. Linear matrix inequality approach is used to solve the finite-time stability problem. Numerical example is given to illustrate the effectiveness of the proposed result.
Robust H∞ Performance of Discrete-time Neural Networks with Uncertainty and Time-varying Delay
M. Syed Ali,K. Meenakshi,R. Vadivel,권오민 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.4
In this paper, we are concerned with the robust H∞ problem for a class of discrete -time neural networks with uncertainties. Under a weak assumption on the activation functional, some novel summation inequality techniques and using a new Lyapunov-Krasovskii (L-K) functional, a delay-dependent condition guaranteeing the robust asymptotically stability of the concerned neural networks is obtained in terms of a Linear Matrix Inequality(LMI). It is shown that this stability condition is less conservative than some previous ones in the literature. The controller gains can be derived by solving a set of LMIs. Finally, two numerical examples result are given to illustrate the effectiveness of the developed theoretical results.
M. Syed Ali,K. Meenakshi,주영훈 제어·로봇·시스템학회 2018 International Journal of Control, Automation, and Vol.16 No.4
This paper deals with the problem of finite-time H∞ filtering for discrete-time Markovian jump BAM neural networks with time-varying delays. To do this, firstly by choosing a suitable Lyapunov function and using Jensen inequality lemma, sufficient criteria are derived to guarantee that the resulting filtering error system is finitetime bounded. And then the gain matrices of the controller and filter are achieved by solving a feasibility problem in terms of linear matrix inequalities with a fixed parameter. Moreover, we assume that disturbances are described by the jumping parameters are generated from discrete-time homogeneous Markov process. Finally a numerical example is presented to show the effectiveness of the proposed method.