Finite-Time Synchronization for T–S Fuzzy Complex-Valued Inertial Delayed Neural Networks Via Decomposition Approach

This paper is mainly dedicated to the issue of finite-time synchronization of T–S fuzzy complex-valued neural networks with time-varying delays and inertial terms via directly constructing Lyapunov functions with separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial delayed neural networks (CVIDNNs) into the first-order differential equation form. Next, CVIDNNs are developed using T–S fuzzy rules. By using the Lyapunov stability theory, inequality scaling skills and adjustable algebraic criteria for T–S fuzzy CVIDNNs as well as the upper bound of the settling time for synchronization, are derived. Finally, one numerical example with simulations is given to illustrate the effectiveness of our theoretical results.