Finite-Time Input-to-State Stability of Neural Networks With Disturbances and Prescribed Performance
研究了带扰动和时滞的神经网络的有限时间输入-状态稳定性，通过引入有限时间收缩稳定性概念实现预设性能，并给出稳定性条件和控制策略，仿真验证了有效性。
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.