Global synchronization of time-invariant uncertainty fractional-order neural networks with time delay

被引：29

作者：

Hu, Taotao ^{[1
]}

He, Zheng ^{[1
]}

Zhang, Xiaojun ^{[2
]}

Zhong, Shouming ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 611731, Sichuan, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China

来源：

NEUROCOMPUTING | 2019年 / 339卷

基金：

中国国家自然科学基金;

关键词：

Global synchronization; Fractional-order integral Jensen's inequality; Lyapunov-Krasovskii functions; Fractional-order neural networks; ROBUST STABILITY; EXPONENTIAL STABILITY; LINEAR-SYSTEMS; STABILIZATION; STATE; CHAOS;

D O I：

10.1016/j.neucom.2019.02.020

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper considers the global synchronization problem of time-invariant uncertainty fractional-order neural networks with time delay. First, the time-invariant uncertain items are converted into the positive real uncertainties. Then, in order to deal with time delay terms, a novel free-matrix-based fractional-order integral inequality (FMBFII) is proposed by using the fractional-order Leibniz-Newton formula and a new class of Lyapunov-Krasovskii functions is constructed. Next, based on FMBFII, Lyapunov-Krasovskii functions and fractional-order integral Jensen's inequality, several global synchronization criteria for time-invariant uncertainty fractional-order neural networks with time delay are studied. Furthermore, compared to the previous fractional-order integral Jensen's inequality, the advantage of the proposed FMBFII is theoretically analyzed. Finally, by using two examples, the feasibility and effectiveness of our proposed results are tested. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：45 / 58

页数：14

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