Practical fixed-time synchronization of fuzzy inertial neural networks with mixed time-varying delays: A parameterized solution approach

Abstract

This paper addresses the unresolved problem of practical fixed-time synchronization (PFTS) with prescribed precision for fuzzy inertial neural networks (FINNs) subject to mixed time-varying delays (leakage, transmission, and distributed delays). Unlike existing studies that rely on Lyapunov–Krasovskii functionals, we propose a novel parameterized solution-based direct analysis approach, that eliminates variable substitution and reduces computational complexity. Key contributions include (1) first theoretical framework for PFTS in FINNs with leakage delays; (2) derivation of settling-time inequalities through scalar conditions; and (3) experimental validation showing faster convergence than prior methods. Numerical simulations demonstrate the efficacy under a precision threshold $\mathit{ϵ}\mathbf{=}\mathbf{0}\mathbf{.}\mathbf{1}$ with convergence time ${\mathit{T}}^{\mathbf{\ast }}\mathbf{\approx }\mathbf{7}\mathbf{.}\mathbf{6407}$ .