Nonfragile Exponential Synchronization for Delayed Fuzzy Memristive Inertial Neural Networks via Memory Sampled-Data Control
Runan Guo, Shengyuan Xu, Baoyong Zhang, Qian Ma
Abstract
This paper studies the exponential synchronization problem of a class of fuzzy memristive inertial neural networks (FMINNs) with time-varying delays. Considering the controller gain fluctuation and transmission delay, nonfragile memory sampled-data control is firstly employed to solve the synchronization of FMINNs. This work does not use the variable conversion technique, but directly performs efficient analysis of the system. An improved fuzzy membership functions-dependent Lyapunov-Krasovskii functional with two-sided looped-functional is designed, which is based on the entire sampling period, and includes the current states and delayed states information. Then, a fuzzy sampled-data controller with a switching topology is designed, and synchronization criteria are established, which take the excitatory and inhibitory of memristive synaptic weights into account. Finally, the effectiveness of the proposed method and the practicability of the addressed model are verified through numerical results.