Sampled-data control-based stabilization of fuzzy inertial quaternion-valued delayed neural networks with parameter uncertainties.

Abstract

This paper addresses the exponential stabilization of fuzzy inertial quaternion-valued neural networks (FIQVNNs) with time-varying delay and parametric uncertainties. To this end, a non-fragile sampled-data controller incorporating a time-delay term is then designed for the first time to ensure the stability of FIQVNNs. Later, to further reduce conservatism, the improved reciprocally convex inequality is extended to quaternion domain. Furthermore, through constructing appropriate Lyapunov-Krasovskii functionals (LKFs) and utilizing advanced inequality techniques, a refined analytical framework is developed. The derived sufficient stability conditions are presented in the form of linear matrix inequalities (LMIs), which provide standards for system stability analysis. Finally, the proposed results are validated through both numerical simulations and an application example.