数学
理论(学习稳定性)
控制理论(社会学)
人工神经网络
线性矩阵不等式
订单(交换)
基质(化学分析)
保守主义
应用数学
数学优化
计算机科学
控制(管理)
人工智能
法学
材料科学
财务
机器学习
政治
政治学
经济
复合材料
作者
Yuehua Cheng,XU Wen-bo,Haitao Jia,Shouming Zhong
标识
DOI:10.1016/j.jfranklin.2023.08.005
摘要
The stability problem of fractional-order neural networks in consideration of time-varying delay is addressed by utilizing Lyapunov functional approach in this paper. Firstly, a class of novel Lyapunov-Krasovskii functions are designed to deal with the time-varying delay terms, which reduces the conservatism of the stability criteria. In addition, to estimate the fractional-order derivative of the Lyapunov-Krasovskii functions, a novel free-matrix-based fractional-order integral inequality is then established, which encompasses the Wirtinger one and leads to tractable linear matrix inequality criteria. Then, based on the designed adaptive control and the proposed Lyapunov-Krasovskii functions, some stability criteria depending on time-varying delay information and fractional-order α are deduced. Finally, numerical simulation shows that the presented approach can significantly reduce the conservatism of the existing results and has a broader application prospect.
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