The Study Of Synchronization Control For Memristive Neural Networks

Compared with the traditional neural networks,the memristive neural networks are more expected to construct a highly integrated neural morphological system that are closer to the size and structure of the human brain.Thus,it can effectively mimic the memory,learning and judgment functions of human brain cells.The memristive neural networks have memory,adaptability and high parallel processing ability.The synchronization control of memristive neural networks is widely used in the fields of pattern recognition,combinatorial optimization,secure communication,function approximation and so on.In real synchronous control systems,sometimes the signal transmission depends on the general communication network.For example,the secure communication in wireless network,the cooperation of robots in the harsh environment,and multi-agent distributed queue control.The memristive neural networks are applied to the networked synchronous control systems,which are modeled and analyzed.Through the remote control and remote operation of control system,more applications can be realized.In addition,the synchronization of complex dynamic networks is widely used in communication networks,biological systems,power systems and information science.The complex dynamic networks with memristive neural networks nodes can simulate the structure and function of brain neural system at different levels,and can be used in robot path planning,the cooperative control of unmanned aerial vehicle,secure communication and satellite formation.Applying the memristive neural networks to the complex networks,the complex networks with memristive neural networks nodes are modeled,and the synchronization characteristic between the complex networks and the independent memristive neural networks under the function of the controller is analyzed.Furthermore,the distributed event-triggered synchronization control of complex networks with memristive neural networks nodes is studied.In this paper,the synchronization control of memristive neural networks is studied.The main contents are as follows:1)Network-based synchronization control of memristive neural networks.The error system model based on communication network is established considering the problems of delay,packet loss and random perturbation in the network.By using the Lyapunov method and the linear matrix inequality(LMI)technique,a delay-dependent sufficient condition for global asymptotically synchronization of error systems in the mean square sense is obtained,and the corresponding controller parameters can be obtained by the LMI toolbox.2)Event-triggered synchronization control of memristive neural networks.An output-based event trigger is introduced into the network-based synchronous control system.An event-triggered output-feedback controller is designed to exponentially synchronize the state of the drive system and the response system.At the same time,the controller can guarantee the H_?performance of the state error.3)Synchronization control of complex networks with memristive neural networks nodes.The problem of synchronization control for complex memristive neural networks under the action of controller is studied.The synchronization condition and controller design method are given in the form of LMI by using Cartesian product technique and Lyapunov method.4)Distributed event-triggered synchronization control of complex memristive neural networks.Multiple event-triggered conditions are designed for multiple sub-nodes of complex memristive neural networks.Under the mechanism of data paket process(DPP),the error system is modeled as a time-delay system,and a sufficient condition to ensure the asymptotic stability of the error system is given by using the Lyapunov functional method.The collaborative design of distributed event-triggered condition and controller based on LMI is proposed.