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Noise-Suppressing Neural Dynamics for Time-Dependent Constrained Nonlinear Optimization With Applications

文献类型：期刊论文

作者Wei, Lin2; Jin, Long2; Luo, Xin1,3
刊名IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
出版日期2022-01-05
页码12
关键词Optimization Mathematical models Analytical models Convergence Principal component analysis Time-varying systems Nonlinear equations Constrained nonlinear optimization dimensionality reduction on principal component analyses (PCA) equality and inequality constraints neural dynamics noise suppression robot arm control
ISSN号2168-2216
DOI10.1109/TSMC.2021.3138550
通讯作者Jin, Long(longjin@ieee.org) ; Luo, Xin(luoxin21@cigit.ac.cn)
英文摘要Up to date, the existing methods for nonlinear optimization with time-dependent parameters can be classified into two types: 1) static methods are capable of handling inequality constraints but may generate large lagging errors in the solution of the intrinsically time-dependent constrained nonlinear optimization (TDCNO) problem due to the hypothesis of short-time invariance and 2) time-variant methods, e.g., zeroing neural networks, are able to remedy the lagging error but fail to solve the TDCNO problem under inequality constraints. To resolve this contradiction, a noise-suppressing neural dynamics (NSND) model is proposed to solve the TDCNO problem subject to both equality and inequality constraints via the nonlinear complementary problem (NCP) function. The proposed method allows inequality constraints for unknown variables, removes the short-time invariance hypothesis, and further eliminates lagging errors during the solving process in the presence of noises. Besides, the rapid convergence, global stability, and noise processing of the NSND model are verified by the theoretical analyses. Simulation results of illustrative examples, including dimensionality reduction on principal component analyses (PCA) and a robot motion control, show that the NSND model outperforms the existing models for the TDCNO problem.
资助项目National Natural Science Foundation of China[62176109] ; Natural Science Foundation of Gansu Province[21JR7RA531] ; Natural Science Foundation of Gansu Province[20JR10RA639] ; Special Projects of the Centra Government in Guidance of Local Science and Technology Development ; Team Project of Natural Science Foundation of Qinghai Province (China)[2020-ZJ-903] ; Key Laboratory of IoT of Qinghai[2020-ZJ-Y16] ; Gansu Provincial Youth Doctoral Fund of Colleges and Universities[2021QB-003] ; Fundamental Research Funds for the Central Universities[lzujbky2021-it36] ; Fundamental Research Funds for the Central Universities[lzujbky-2021-65] ; Supercomputing Center of Lanzhou University ; Science and Technology Project of Chengguan District of Lanzhou[2021-1-2] ; Science and Technology Project of Chengguan District of Lanzhou[2021-7-1] ; Gansu Provincial Department of Education: Outstanding Graduate Student Star of Innovation Project[2021CXZX-033] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
WOS记录号WOS:000740072100001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/14981]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Jin, Long; Luo, Xin
作者单位1.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
2.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
3.Chinese Acad Sci, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing Inst Green & Intelligent Technol, Chongqing 400714, Peoples R China
推荐引用方式
GB/T 7714		 Wei, Lin,Jin, Long,Luo, Xin. Noise-Suppressing Neural Dynamics for Time-Dependent Constrained Nonlinear Optimization With Applications[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2022:12.
APA Wei, Lin,Jin, Long,&Luo, Xin.(2022).Noise-Suppressing Neural Dynamics for Time-Dependent Constrained Nonlinear Optimization With Applications.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,12.
MLA Wei, Lin,et al."Noise-Suppressing Neural Dynamics for Time-Dependent Constrained Nonlinear Optimization With Applications".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2022):12.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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