Stability Analysis Of Stochastic Neutral Neural Network

This article mainly discusses the stability problem of a series of stochastic neutralneural network driven by wiener process. By constructing Lyapunov-Krasovskiifunctional and using Ito formula and linear matrix inequality technique, here studies akind of stochastic global asymptotic stability of neutral neural network, theexponential stability and the robust stability, and gives the stability criterion which iseasy to verify.Based on the previous literature the related results are generalized.In this paper, the primary content is as follows:1.The research background,present situation and dynamic of stochastic stability ofneutral neural network are introduced.2.By constructing Lyapunov-Krasovskii functional and by means of stochasticanalysis techniques,dynamic behavior of a class of stochastic global asymptoticstability of neutral neural network and robust stability is studied, determining stabilityconditions are given. The corresponding results of predecessors are popularized.3.Using Lyapunov-Krasovskii functional and combining linear inequality researchmethods,a class of stochastic neural network neutrality exponential stability and therobust exponential stability problems are researched, and the algebraic criterion whichis simple to test is given. The relevant results in the dependent literatures arepromoted.4.The exponential stability problems on a class of stochastic neutral static neuralnetwork are discussed, and discriminant index stability criterion is given.5.The problems which are further researched are prospected.