A mode-dependent stability criterion for delayed discrete-time stochastic neural networks with Markovian jumping parameters

Authors:
Yan Ou;Peng Shi;Hongyang Liu
Affiliations:
Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang 150001, PR China;Faculty of Advanced Technology, University of Glamorgan, CF37 1DL Pontypridd, UK;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, Heilongjiang 150001, PR China
Venue:
Neurocomputing
Year:
2010

Citing 12
Cited 4

Optimal guaranteed cost control of uncertain discrete time-delay systems

Journal of Computational and Applied Mathematics
Robust stability of stochastic delayed additive neural networks with Markovian switching

Neural Networks
Brief paper: Robust exponential stabilization for Markovian jump systems with mode-dependent input delay

Automatica (Journal of IFAC)
Robust stability of discrete-time stochastic neural networks with time-varying delays

Neurocomputing
Robust delay-distribution-dependent stability of discrete-time stochastic neural networks with time-varying delay

Neurocomputing
State estimation for jumping recurrent neural networks with discrete and distributed delays

Neural Networks
Letters: Mean square exponential stability of stochastic fuzzy Hopfield neural networks with discrete and distributed time-varying delays

Neurocomputing
Robust stability for uncertain delayed fuzzy hopfield neural networks with Markovian jumping parameters

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays: the discrete-time case

IEEE Transactions on Neural Networks
Stability and synchronization of discrete-time Markovian jumping neural networks with mixed mode-dependent time delays

IEEE Transactions on Neural Networks
A note on the robust stability of uncertain stochastic fuzzy systems with time-delays

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Technical Communique: Robust H∞ observer design of linear state delayed systems with parametric uncertainty: the discrete-time case

Automatica (Journal of IFAC)

Stability of stochastic Markovian jump neural networks with mode-dependent delays

Neurocomputing
Exponential stability of impulsive discrete systems with time delay and applications in stochastic neural networks: A Razumikhin approach

Neurocomputing
Stability analysis of standard genetic regulatory networks with time-varying delays and stochastic perturbations

Neurocomputing
State Estimation for Discrete-Time Neural Networks with Markov-Mode-Dependent Lower and Upper Bounds on the Distributed Delays

Neural Processing Letters

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper investigates the problem of stability for a class of discrete-time stochastic neural networks (DSNNs) with mode-dependent delay and Markovian jumping parameters. Throughout this paper, we assume that stochastic disturbances are described by the Brownian motion, jumping parameters are generated from discrete-time discrete-state homogeneous Markov process, and mode-dependent delay d(r(k)) satisfies d"m@?d(r(k))@?d"M. By a novel Lyapunov-Krasovskii functional combining with the delay partitioning technique and the free-weighting matrix method in terms of linear matrix inequalities (LMIs), the new stability criterion proves to be less conservative. Finally, numerical examples are given to illustrate the effectiveness of the proposed method.