Fuzzy-Based Bipartite Quasi-Synchronization of Fractional-Order Heterogeneous Reaction-Diffusion Neural Networks via Intermittent Control
Yao Xu, Zhuozhen Jiang, Xiangpeng Xie, Wenxue Li, Yongbao Wu
Abstract
This paper investigates the T-S fuzzy-based bipartite quasi-synchronization of fractional-order heterogeneous coupled reaction-diffusion neural networks. In the considered neural networks, interactions between adjacent neurons are time-varying, cooperative, and competitive, and heterogeneity and T-S fuzzy system rule are simultaneously introduced to characterize the parameter uncertainty arising from complexity and ambiguity in the real world. A new time-varying graph-theoretic Lyapunov function is given for time-varying coupled reaction-diffusion neural networks. Meanwhile, a more general fractional-order derivative law is provided to estimate the derivative of this function, which includes the existing fractional-order derivative laws. Based on a fuzzy-based aperiodically intermittent control, some sufficient conditions are offered for the bipartite quasi-synchronization under a time-varying graph-theoretic Lyapunov function, and the allowable error bound is given. Finally, we carry out some simulations numerically to show the validity of the theory.