Asymptotic Stability of Fractional-Order Incommensurate Neural Networks

被引：3
|
作者：
Chen, Liping [1 ]
Gu, Panpan [1 ]
Lopes, Antonio M. [2 ]
Chai, Yi [3 ]
Xu, Shuiqing [1 ]
Ge, Suoliang [1 ]
机构：
[1] Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Peoples R China
[2] Univ Porto, Fac Engn, LAETA, INEGI, Rua Dr Roberto Frias, P-4200465 Porto, Portugal
[3] Chongqing Univ, Sch Automat, Chongqing 400044, Peoples R China
来源：
NEURAL PROCESSING LETTERS | 2023年 / 55卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Fractional-order systems; Neural networks; Stability; Multi-order systems; FINITE-TIME STABILITY; UNIFORM STABILITY; ROBUST STABILITY; SYSTEMS; MULTISTABILITY; DYNAMICS; CRITERIA; CHAOS;
D O I：
10.1007/s11063-022-11095-y
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The dynamics and stability of fractional-order (FO) neural networks (FONN) and FO memristive neural networks (FOMNN), have received great attention in the last years. However, most research focused merely on commensurate FONN (all neurons have the same order). This paper addresses the stability of a class of incommensurate FONN for the first time. Firstly, using the comparison principle for FO systems with multi-order, the stability of FO nonlinear systems with multi-order is treated similarly to the stability of incommensurate FO linear systems. Then, adopting the stability results of incommensurate FO linear systems, an asymptotic stability criterion for FONN is established. The proposed method is valid for investigating the stability and synchronization of uncertain FONN and FOMNN with multi-order. Numerical simulations illustrate the theoretical results and their effectiveness.
引用
收藏
页码：5499 / 5513
页数：15
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