Global Exponential Synchronization of the Memristor-Based Fuzzy Cellular Neural Networks via the Delayed Impulsive Control - MU Xiaohui;TANG Rongqiang;YANG Xinsong (Nantong Daxue xuebao. Ziran kexue ban)
The paper studies the global exponential synchronization problem of the memristor-based fuzzy cellular neural networks. First, by utilizing the Filippov solution and the measurable selection theorems, the memristor-based fuzzy cellular neural network is transformed into a kind of neural network with the uncertain parameters. In addition,the novel fuzzy inequalities are given to solve the parameter uncertainty problem of the fuzzy feedback connection weight. Then, by designing a delayed impulsive controller and utilizing the concept of the Filippov solution, the delayed impulsive inequalities and the novel fuzzy inequalities, the results of the exponential synchronization between the driving memristor-based fuzzy cellular neural networks and the response memristor-based fuzzy cellular neural networks are obtained. Finally, a numerical example is given to illustrate the effectiveness of theoretical results. The results show that the drive-response system of the memristor-based fuzzy cellular neural networks can be exponentially synchronized by designing a proper controller.