Fuzzy spatiotemporal event-triggered control for the synchronization of IT2 T–S fuzzy CVRDNNs with mini-batch machine learning supervision

Abstract

This paper is centered on the development of a fuzzy memory-based spatiotemporal event-triggered mechanism (FMSETM) for the synchronization of the drive-response interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy complex-valued reaction–diffusion neural networks (CVRDNNs). CVRDNNs have a higher processing capability and can perform better than multilayered real-valued RDNNs. Firstly, a general IT2 T–S fuzzy neural network model is constructed by considering complex-valued parameters and the reaction–diffusion terms. Secondly, a mini-batch semi-stochastic machine learning technique is proposed to optimize the maximum sampling period in an FMSETM. Furthermore, by constructing an asymmetric Lyapunov functional (LF) dependent on the membership function (MF), certain symmetric and positive-definite constraints of matrices are removed. The synchronization criteria are derived via linear matrix inequalities (LMIs) for the IT2 T–S fuzzy CVRDNNs. Finally, two numerical examples are utilized to corroborate the feasibility of the developed approach. From the simulation results, it can be seen that introducing machine learning techniques into the synchronization problem of CVRDNNs can improve the efficiency of convergence.