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RNN for Solving Perturbed Time-Varying Underdetermined Linear System With Double Bound Limits on Residual Errors and State Variables

文献类型：期刊论文

作者Lu, Huiyan1,2; Jin, Long1,2; Luo, Xin3,4; Liao, Bolin5; Guo, Dongsheng6; Xiao, Lin7
刊名IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
出版日期2019-11-01
卷号15期号:11页码:5931-5942
关键词Mathematical model Time-varying systems Linear systems Informatics Robots Recurrent neural networks Double bound limits recurrent neural network simulation results theoretical analyses time-varying underdetermined linear system
ISSN号1551-3203
DOI10.1109/TII.2019.2909142
通讯作者Jin, Long(longjin@ieee.org)
英文摘要Neural networks have been generally deemed as important tools to handle kinds of online computing problems in recent decades, which have plenty of applications in science and electronics fields. This paper proposes a novel recurrent neural network (RNN) to handle the perturbed time-varying underdetermined linear system with double bound limits on residual errors and state variables. Beyond that, the bound-limited underdetermined linear system is converted into a time-varying system that consists of linear and nonlinear formulas through constructing a nonnegative time-varying variable. Then, theoretical analyses are conducted to verify the superior convergence performance of the proposed RNN model. Furthermore, numerical experiment results and computer simulations demonstrate the superiority and effectiveness of the proposed RNN model for handling the time-varying underdetermined linear system with double bound limits. Finally, the proposed RNN model is applied to the physically limited PUMA560 robot to show its satisfactory applicabilities.
资助项目National Natural Science Foundation of China[61703189] ; Fund of Key Laboratory of Industrial Internet of Things and Networked Control, Ministry of Education, China[2018FF06] ; International Science and Technology Cooperation Program of China[2017YFE0118900] ; Natural Science Foundation of Gansu Province, China[18JR3RA264] ; Natural Science Foundation of Gansu Province, China[18JR3RA268] ; Sichuan Science and Technology Program[19YYJC1656] ; Fundamental Research Funds for the Central Universities[lzujbky-2017-193] ; Natural Science Foundation of Hunan Province[2017JJ3257] ; Research Foundation of Education Bureau of Hunan Province, China[17C1299]
WOS研究方向Automation & Control Systems ; Computer Science ; Engineering
语种英语
WOS记录号WOS:000498643600013
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/10048]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Jin, Long
作者单位1.Minist Educ, Key Lab Ind Internet Things & Networked Control, Chongqing 400000, Peoples R China
2.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China
3.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
4.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
5.Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Peoples R China
6.Huaqiao Univ, Coll Informat Sci & Engn, Xiamen 361000, Fujian, Peoples R China
7.Hunan Normal Univ, Hunan Prov Key Lab Intelligent Comp & Language In, Changsha 410081, Hunan, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Huiyan,Jin, Long,Luo, Xin,et al. RNN for Solving Perturbed Time-Varying Underdetermined Linear System With Double Bound Limits on Residual Errors and State Variables[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2019,15(11):5931-5942.
APA Lu, Huiyan,Jin, Long,Luo, Xin,Liao, Bolin,Guo, Dongsheng,&Xiao, Lin.(2019).RNN for Solving Perturbed Time-Varying Underdetermined Linear System With Double Bound Limits on Residual Errors and State Variables.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,15(11),5931-5942.
MLA Lu, Huiyan,et al."RNN for Solving Perturbed Time-Varying Underdetermined Linear System With Double Bound Limits on Residual Errors and State Variables".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 15.11(2019):5931-5942.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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