A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks
Zhang, Huaguang1,2; Wang, Zhanshan1,2; Liu, Derong3
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2014-07-01
Volume25Issue:7Pages:1229-1262
SubtypeReview
AbstractStability problems of continuous-time recurrent neural networks have been extensively studied, and many papers have been published in the literature. The purpose of this paper is to provide a comprehensive review of the research on stability of continuous-time recurrent neural networks, including Hopfield neural networks, Cohen-Grossberg neural networks, and related models. Since time delay is inevitable in practice, stability results of recurrent neural networks with different classes of time delays are reviewed in detail. For the case of delay-dependent stability, the results on how to deal with the constant/variable delay in recurrent neural networks are summarized. The relationship among stability results in different forms, such as algebraic inequality forms, M-matrix forms, linear matrix inequality forms, and Lyapunov diagonal stability forms, is discussed and compared. Some necessary and sufficient stability conditions for recurrent neural networks without time delays are also discussed. Concluding remarks and future directions of stability analysis of recurrent neural networks are given.
KeywordCohen-grossberg Neural Networks Discrete Delay Distributed Delays Hopfield Neural Networks Linear Matrix Inequality (Lmi) Lyapunov Diagonal Stability (Lds) M-matrix Recurrent Neural Networks Robust Stability Stability
WOS HeadingsScience & Technology ; Technology
WOS KeywordGLOBAL ASYMPTOTIC STABILITY ; ABSOLUTE EXPONENTIAL STABILITY ; DESCRIPTOR SYSTEM APPROACH ; DELAY-DEPENDENT STABILITY ; REACTION-DIFFUSION TERMS ; H-INFINITY CONTROL ; COHEN-GROSSBERG MODEL ; DISCONTINUOUS ACTIVATION FUNCTIONS ; FUNCTIONAL-DIFFERENTIAL EQUATIONS ; DERIVING SUFFICIENT CONDITIONS
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000337906300001
Citation statistics
Cited Times:503[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3839
Collection多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
Affiliation1.Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China
2.State Key Lab Synthet Automat Proc Ind, Shenyang 110004, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Huaguang,Wang, Zhanshan,Liu, Derong. A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2014,25(7):1229-1262.
APA Zhang, Huaguang,Wang, Zhanshan,&Liu, Derong.(2014).A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,25(7),1229-1262.
MLA Zhang, Huaguang,et al."A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 25.7(2014):1229-1262.
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