title: Stochastic stability of discrete-time Markovian jump delay neural networks with impulses and incomplete information on transition probability.
abstract: The purpose of this letter is to investigate the stochastic stability for a class of discrete-time Markovian jump delay neural networks with impulses and incomplete information on transition probability. By using Lyapunov functionals, some new results are provided. The obtained results show that impulses can stochastically stabilize an unstable discrete-time Markovian jump delay neural network. The obtained results also show that the stability property of the impulse-free neural network can be retained even under certain destabilizing impulses. Two examples together with their simulations are also presented to show the effectiveness and the advantage of the obtained results.
authors.affiliation: Department of Mathematics, Tongji University, Shanghai, 200092, PR China. zhangyu2008@tongji.edu.cn
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008390:Markov Chains; D016571:Neural Networks, Computer; D011336:Probability; D013997:Time Factors
keywords: Delay; Discrete-time neural network; Impulse; Lyapunov functional; Markovian jump parameter