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Pseudoinverse-Free Recurrent Neural Dynamics for Time-Dependent System of Linear Equations With Constraints on Variable and Its Derivatives
L. Jin; W. Du; D. Ma; L. Jiao; S. Li
2024-05-10
Online publication date2024-05
Source PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN2168-2232
EISSN2168-2232
VolumePPIssue:99Pages:1-10
page numbers10
AbstractRecently, recurrent neural networks have been extensively utilized to address a time-dependent system of linear equations (TDSLEs) with inequality systems. Nevertheless, these existing studies only limit the variable without considering constraints on its derivatives, which may be challenging to accomplish a given task in practical applications when additional constraints are introduced. Beyond that, the matrix pseudoinverse is performed, and non-negative slack variables are introduced in the solution process, which increases the model’s complexity and leads to a high computational burden. To remedy these deficiencies, this article makes improvements via proposing a novel recurrent neural dynamics (RND) model for solving the TDSLEs with constraints on the variable and its derivatives. Specifically, such a model neither needs to compute the pseudoinverse of a matrix nor to introduce non-negative slack variables, thereby enhancing its computational efficiency and accuracy. Corresponding theoretical analysis is provided to ensure its convergence performance. Finally, numerical results, comparisons with other models, and applications to single and multiple robots are provided, which substantiates the availability and meliority of the pseudoinverse-free RND model for disposing of the TDSLEs with constraints on the variable and its derivatives.
KeywordConstraints on the variable and its derivatives multirobot application recurrent neural dynamics (RND) time-dependent system of linear equations (TDSLEs)
PublisherIEEE
DOI10.1109/TSMC.2024.3392210
Indexed ByIEEE ; SCIE
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Cybernetics
WOS IDWOS:001218598600001
Original Document TypeArticle ; Early Access
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/588983
Collection兰州大学
信息科学与工程学院
Affiliation
1.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
2.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
3.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
4.Science and Technology on Communication Networks Laboratory, The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang, China
5.School of Information Science and Engineering, Lanzhou University, Lanzhou, China
Recommended Citation
GB/T 7714
L. Jin,W. Du,D. Ma,et al. Pseudoinverse-Free Recurrent Neural Dynamics for Time-Dependent System of Linear Equations With Constraints on Variable and Its Derivatives[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems,2024,PP(99):1-10.
APA L. Jin,W. Du,D. Ma,L. Jiao,&S. Li.(2024).Pseudoinverse-Free Recurrent Neural Dynamics for Time-Dependent System of Linear Equations With Constraints on Variable and Its Derivatives.IEEE Transactions on Systems, Man, and Cybernetics: Systems,PP(99),1-10.
MLA L. Jin,et al."Pseudoinverse-Free Recurrent Neural Dynamics for Time-Dependent System of Linear Equations With Constraints on Variable and Its Derivatives".IEEE Transactions on Systems, Man, and Cybernetics: Systems PP.99(2024):1-10.
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