Finite-Time Multiparty Synchronization of T-S Fuzzy Coupled Memristive Neural Networks With Optimal Event-Triggered Control

In this article, we consider multiparty synchronization (MS) for coupled memristive neural networks (CMNNs) with a time delay. Some Takagi-Sugeno type if-then fuzzy rules are also introduced into the CMNNs. An event-triggered controller (ETC) is designed to achieve the MS in finite time, which avoids continuous control signals. Along with Lyapunov theory, differential inclusion theory, and inequalities, some criteria can be obtained to achieve the finite-time MS (FTMS), and the setting time (ST) of the FTMS can be calculated. By jointly considering ST, control inputs, error networks, and synchronization conditions, an optimization model is provided to get an optimal ETC (OETC). Further, the particle swarm optimization algorithm is utilized to solve the optimization model. Thus, it gives a method to choose control gains and parameters of an event-triggered function. Finally, two examples are given to verify the theoretical results. Especially, two comparative experiments are proposed to demonstrate that the OETC can save more control energy and reduce the number of triggered times.