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Relaxed Stability Criteria for Neural Networks With Time-Varying Delay Using Extended Secondary Delay Partitioning and Equivalent Reciprocal Convex Combination Techniques

文献类型：期刊论文

作者Wang, Shenquan2,3; Ji, Wenchengyu3; Jiang, Yulian3; Liu, Derong1
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2020-10-01
卷号31期号:10页码:4157-4169
关键词Delays Asymptotic stability Artificial neural networks Linear matrix inequalities Stability criteria Automation Equivalent reciprocal convex combination extended secondary delay partitioning global asymptotic stability neural networks (NNs) time-varying delay
ISSN号2162-237X
DOI10.1109/TNNLS.2019.2952410
通讯作者Liu, Derong(derong@gdut.edu.cn)
英文摘要This article investigates global asymptotic stability for neural networks (NNs) with time-varying delay, which is differentiable and uniformly bounded, and the delay derivative exists and is upper-bounded. First, we propose the extended secondary delay partitioning technique to construct the novel Lyapunov-Krasovskii functional, where both single-integral and double-integral state variables are considered, while the single-integral ones are only solved by the traditional secondary delay partitioning. Second, a novel free-weight matrix equality (FWME) is presented to resolve the reciprocal convex combination problem equivalently and directly without Schur complement, which eliminates the need of positive definite matrices, and is less conservative and restrictive compared with various improved reciprocal convex inequalities. Furthermore, by the present extended secondary delay partitioning, equivalent reciprocal convex combination technique, and Bessel-Legendre inequality, two different relaxed sufficient conditions ensuring global asymptotic stability for NNs are obtained, for time-varying delays, respectively, with unknown and known lower bounds of the delay derivative. Finally, two examples are given to illustrate the superiority and effectiveness of the presented method.
WOS关键词GLOBAL ASYMPTOTIC STABILITY ; SYSTEMS ; SYNCHRONIZATION
资助项目National Natural Science Foundation of China[61503045] ; National Natural Science Foundation of Jilin Province[20180101333JC] ; State Key Laboratory of Management and Control for Complex Systems (SKLMCCS), Institute of Automation, Chinese Academy of Sciences ; SKLMCCS[20190104]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000576436600031
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China ; National Natural Science Foundation of Jilin Province ; State Key Laboratory of Management and Control for Complex Systems (SKLMCCS), Institute of Automation, Chinese Academy of Sciences ; SKLMCCS
源URL[http://ir.ia.ac.cn/handle/173211/42084]  
专题自动化研究所_复杂系统管理与控制国家重点实验室
通讯作者Liu, Derong
作者单位1.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Changchun Univ Technol, Coll Elect & Elect Engn, Changchun 130012, South Korea
推荐引用方式
GB/T 7714		 Wang, Shenquan,Ji, Wenchengyu,Jiang, Yulian,et al. Relaxed Stability Criteria for Neural Networks With Time-Varying Delay Using Extended Secondary Delay Partitioning and Equivalent Reciprocal Convex Combination Techniques[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(10):4157-4169.
APA Wang, Shenquan,Ji, Wenchengyu,Jiang, Yulian,&Liu, Derong.(2020).Relaxed Stability Criteria for Neural Networks With Time-Varying Delay Using Extended Secondary Delay Partitioning and Equivalent Reciprocal Convex Combination Techniques.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(10),4157-4169.
MLA Wang, Shenquan,et al."Relaxed Stability Criteria for Neural Networks With Time-Varying Delay Using Extended Secondary Delay Partitioning and Equivalent Reciprocal Convex Combination Techniques".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.10(2020):4157-4169.

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来源：自动化研究所

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