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

doi:10.1109/TNNLS.2019.2952410

	Relaxed Stability Criteria for Neural Networks With Time-Varying Delay Using Extended Secondary Delay Partitioning and Equivalent Reciprocal Convex Combination Techniques
	Wang, Shenquan1,2 ; Ji, Wenchengyu 1; Jiang, Yulian 1; Liu, Derong 3
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2020-10-01
卷号	31 期号:10 页码:4157-4169
通讯作者	Liu, Derong(derong@gdut.edu.cn)
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.
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
DOI	10.1109/TNNLS.2019.2952410
关键词[WOS]	GLOBAL ASYMPTOTIC STABILITY ; SYSTEMS ; SYNCHRONIZATION
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000576436600031
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42085
专题	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.Changchun Univ Technol, Coll Elect & Elect Engn, Changchun 130012, South Korea 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Peoples R China
第一作者单位	中国科学院自动化研究所
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.