数学
符号
理论(学习稳定性)
格朗沃尔不等式
分数阶微积分
结合属性
应用数学
算法
纯数学
离散数学
不平等
域代数上的
计算机科学
数学分析
算术
机器学习
作者
XD Gao,Yajing Li,Xinyi Liu,Yinlin Ye,Hongtao Fan
标识
DOI:10.1109/tnnls.2023.3335267
摘要
This article investigates the finite-time stability of a class of fractional-order bidirectional associative memory neural networks (FOBAMNNs) with multiple proportional and distributed delays. Different from the existing Gronwall integral inequality with single proportional delay ( $N = 1$ ), we establish the Gronwall integral inequality with multiple proportional delays for the first time in the case of $N \geq 2$ . Since the existing fractional-order single-constant delay Gronwall inequality with two different orders cannot be directly applied to the stability analysis of the aforementioned system, initially, we skillfully develop a novel one with generalized fractional multiproportional delays’ Gronwall inequalities of different orders. Furthermore, combined with the newly constructed generalized inequality, the stability criteria of FOBAMNNs with fractional orders $0<\alpha<1$ and $1<\alpha<2$ under weaker conditions, i.e., at most linear growth and linear growth conditions rather than the global Lipschitz condition, are given respectively. Finally, numerical experiments verify the effectiveness of the proposed method.
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