Synchronization Control And Stability Analysis Of Brain-like Memristive Neural Networks With Associative Memory

The associative memory neural networks can be treated as a brain-like computational model to reflect the cognitive functions of the brain.Due to the characters of the memristor,the brain-like memristive neural networks with associative memory have become a state-dependent differential dynamic system.The dynamic behaviors of such system are more complex and changeable than the normal neural networks,because of the nonlinear character of the memristor and the hierarchical structure of the associative memory neural networks.According to the application trend of memristors in brain-like intelligence research,the dynamics theory of brain-like memristive neural networks with associative memory needs to be improved urgently.Therefore,the synchronization control and stability analysis of brain-like memristive neural networks with associative memory have been studied in this paper.And these studies will provide important technical support and theoretical basis for the realization and application of associative memory neural network in the field of brain-like intelligence.The main contributions and innovation of this paper are as follows:1.Aiming at the issue of synchronization control and stability analysis of brain-like memristive neural networks with unidirectional associative memory,the neural networks model with mixed time-varying delays and impulsive disturbances is proposed.Considering the coupling characteristics of the neural networks,a linear feedback controller and Lyapunov function are designed under the pinning control mechanism,to obtain the sufficient conditions that can ensure the global asymptotic stability of such system.2.The sampling synchronization issue of stochastic brain-like memristive neural networks with bidirectional associative memory is studied.Based on the theory of Lyapunov stability,differential inclusion and set-valued mapping,the discrete-time sampling cont,roller is designed and the stability of the error system is analyzed.Comparing with the continuous-time control method,the system can converge within a shorter time under the proposed sampled-data control.3.Given the synchronization control and stability analysis of the brain-like memristive neural networks with multi-directional associative memory,the definition of lag functional projective and exponential synchronization is proposed.Then,a composite adaptive feedback controller with parameter information is designed to guarantee the global exponential stability of such a system.At the same time,the results can be extended to a variety of synchronization types to satisfy the engineering practicability.4.The brain-like complex-valued memristive neural networks with unidirectional associative memory model are proposed in this paper.Based on the definition of exponential synchronization,a linear feedback controller is designed to solve the synchronization problem of such a system under the uncertain disturbances.Aiming at the application of image secure communication,with the stability analyzed mentioned above,a chaotic image secure mechanism is designed,and a better encryption and decryption performance is achieved.