Finite-Time Input-to-State Stability of Neural Networks With Disturbances and Prescribed Performance
Mingxin Wang, Song Zhu, Xiaoyang Liu, Shiping Wen, Chaoxu Mu · IEEE Transactions on Systems Man and Cybernetics Systems · 2025
In dynamic systems with communication limitations and delay, the input-to-state stability (ISS) is crucial for ensuring system performance. In this article, the finite-time ISS (FTISS) of time-delay neural networks (NNs) with disturbances is investigated. First, by further considering the idea of finite-time contractive stability (FTCS), the prescribed performance is proposed for the considered NNs system, thereby achieving better learning ability and robustness. Next, in order to achieve the above research objectives, some stability conditions for the considered NNs with disturbances are given by constructing sequentially two classes of Lyapunov functions and a finite-time contractive function. Subsequently, a stabilization strategy is proposed to further reduce the parameter requirements of the NNs system and improve its application value. Finally, the numerical simulation and comparative experiments have verified the effectiveness of the stability strategy provided in this article.