Finite-Time Stabilization Criteria of Delayed Inertial Neural Networks with Settling-Time Estimation Protocol and Reliable Control Mechanism
Wenhao Wang, Lanfeng Hua, Hong Yang Zhu, Jun Wang, Kaibo Shi, Shouming Zhong · Fractal and Fractional · 2023
This work investigates the finite-time stability (FTS) issue for a class of inertial neural networks (INNs) with mixed-state time-varying delays, proposing a novel analytical approach. Firstly, we establish a novel FTS lemma, which is entirely different from the existing FTS theorems, and extend the current research results. Secondly, an improved discontinuous reliable control mechanism is developed, which is more valid and widens the application scope compared to previous results. Then, by using a novel non-reduced order approach (NROA) and the Lyapunov functional theory, novel sufficient criteria are established using FTS theorems to estimate the settling time with respect to a finite-time stabilization of INNs. Finally, the simulation results are given to validate the usefulness of the theoretical results.