Stability Analysis And Application Of Stochastic Neutral Neural Network

Since the development of neural network,it has been widely used in automatic control,speech recognition,associative memory,artificial intelligence and many other research fields.Neural network mainly relies on the complexity of the system to process the relationships among a large number of interconnected nodes.Markov process can switch the neural network.As we all know,in various engineering systems,time-delay and parameter uncertainty are often encountered.These factors are often the main reasons for the instability,oscillation and poor performance of the control system,so the research of time-delay system with parameter uncertainty has attracted extensive attention of many researchers in r ecent years.In these research topics,the stability problem is of great significance.Based on the above background,this paper takes the stochastic neutral neural network system with mixed delay Markov jump as the research object.Firstly,by constructing the appropriate Lyapunov-Krasovskii function,using Ito's differential law,and combining with the relevant theory of stochastic process,the sufficient conditions for the mean square asymptotic stability of the system are established and proved.Then,the robust stability of the system is studied.Using Lyapunov stability theorem and linear matrix inequality principle,the criteria of robust asymptotic stability of the system are obtained.Then,the robust stability of two kinds of special systems is also studied in this paper.The sufficient condi tions to guarantee the robust asymptotic stability of these two kinds of special neural network systems are established by using the related theorems.In order to test the validity of the proposed criteria,all the above proof processes are verified by numerical examples The application is briefly analyzed.