Fixed-time synchronization analysis for discontinuous fuzzy inertial neural networks with parameter uncertainties

Abstract

Abstract This paper aims to investigate the fixed-time synchronization analysis for discontinuous fuzzy inertial neural networks in the presence of parameter uncertainties. By using a new variable transformation and differential inclusions theory, we first establish two kinds of drive-response differential inclusion systems. By designing some novel discontinuous control inputs and using Lyapunov-Krasovskii functional approach, some sufficient criteria are derived for achieving fixed-time synchronization, and the corresponding setting times are estimated. The established results provide a new framework to deal with the inertial neural networks with fuzzy logics and discontinuous activation functions. Some previous works in the literature are extended and complement. Finally, two topical simulation examples are given to show the effectiveness of the developed main control schemes.