忆阻型模糊细胞神经网络在时滞脉冲控制下的全局指数同步

文章研究了忆阻型模糊细胞神经网络的全局指数同步控制问题。首先,通过采用Filippo解和可测选择理论将忆阻模糊细胞神经网络转化成一类参数不确定的神经网络,并给出一个新颖的不等式以解决模糊反馈连接权重的参数不确定问题;然后,通过设计时滞脉冲控制器,并结合李雅普诺夫函数法、脉冲不等式以及给出的新的不等式,得到驱动忆阻模糊细胞神经网络与响应忆阻模糊细胞神经网络在时滞脉冲控制下指数同步的结果;最后,通过数值模拟验证了理论结果的有效性。研究结果表明:采用合适的控制器,忆阻型模糊细胞神经网络的驱动-响应系统是可以达到指数同步的。

Global Exponential Synchronization of the Memristor-Based Fuzzy Cellular Neural Networks via the Delayed Impulsive Control

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.