Fixed time stabilization of stochastic neutral-type fuzzy inertial neural network with mixed delays

This paper investigates the fixed-time stabilization problem for a class of stochastic neutral-type fuzzy inertial neural networks subject to mixed delays and unbounded activation functions. Leveraging stochastic analysis and advanced inequality techniques, we establish several sufficient conditions guaranteeing fixed-time stability of the considered model under both feedback and adaptive control schemes. Crucially, these stability criteria are formulated independently of the upper bounds of the activation functions, significantly broadening their applicability to a wider range of systems with unbounded nonlinearities. Moreover, the corresponding settling times associated are analytically estimated. Numerical simulations are finally presented to validate the effectiveness of both proposed controllers.