Synchronous Analysis Of Memristive Neural Networks With Time-varying Delays

With the rapid development of computers,algorithms and software,etc.,the memristive neural networks have a large number of applications in the fields of aviation,electronic engineering,intelligent robots and so on.Simultaneously,memristive neural networks play an important role in the face recognition,modal optimization,intelligent control.Therefore,the research on memristive neural networks not only has important theoretical value,but also has important application value.The main purpose of this paper is to study the synchronization problem of memristive neural networks with time-varying delays.Two kinds of memristive neural network models with time-varying delays are discussed by Lyapunov stability theory and basic mathematical inequalities.The research results will be listed as follows:First,the exponential synchronization problem of memristive neural networks with time-varying delays is studied.The main feature is that the robust analysis method is used to deal with the weight matrix of memristive neural networks.By constructing the appropriate Lyapunov functions,combining with the improved convex combination inequality and linear matrix inequalities,the conditions for the exponential synchronization of the master-slave systems of the memristive neural networks are given.Moreover,in the numerical simulation,not only the feasibility of the given conditions is verified,but also the conclusion that the condition can reduce the conservativeness can be concluded by comparing with other literature.Second,the exponential synchronization problem of delayed memristive neural networks with stochastic disturbance is consideried.The main method is to achieve the synchronization effect on the active system by adding nonlinear feedback control and impulsive control to the driven system.The weight matrix coefficients of the memristive neural networks are processed by taking the maximum absolute value.A simple representation is given so that the synchronization condition can be given in the form of linear matrix inequalities.Utilizing the simple quadratic Lyapunov function,and some basic mathematical methods,the conditions for the master-slave memristive neural network systems to achieve exponential synchronization are given.The effectiveness of the given results is illustrated in a numerical simulation.