Publications (3)1.25 Total impact
Article: Exponential stability in the mean square for stochastic neural networks with mixed time-delays and Markovian jumping parameters[show abstract] [hide abstract]
ABSTRACT: In this paper, the stability analysis problem is considered for a class of stochastic neural networks with mixed time-delays and Markovian jumping parameters. The mixed delays include discrete and distributed time-delays, and the jumping parameters are generated from a continuous-time discrete-state homogeneous Markov process. The aim of this paper is to establish some criteria under which the delayed stochastic neural networks are exponentially stable in the mean square. By constructing suitable Lyapunov functionals, several stability conditions are derived on the basis of inequality techniques and the stochastic analysis. An example is also provided in the end of this paper to demonstrate the usefulness of the proposed criteria.Nonlinear Dynamics 04/2012; 57(1):209-218. · 1.25 Impact Factor
Neurocomputing. 01/2009; 72:3901-3906.
Article: Robust exponential stability analysis for stochastic genetic networks with uncertain parameters[show abstract] [hide abstract]
ABSTRACT: In this paper, the robust exponential stability problem is considered for a class of stochastic genetic networks with uncertain parameters. Under assumptions that the parameter uncertainties are norm bounded, both cases that the genetic network has or has not time delays are discussed. Sufficient conditions are derived to guarantee the robust exponential stability in the mean square of stochastic genetic networks for all admissible parameter uncertainties. By applying Lyapunov function (functional) and conducting some stochastic analysis, the stability criteria are given in the form of linear matrix inequalities (LMI’s), which can be easily checked in practice. Two illustrative examples are also given to show the usefulness of the proposed criteria.Communications in Nonlinear Science and Numerical Simulation.