Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications

doi:10.1109/TNNLS.2019.2944992

	Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications
	Qi, Yimeng 1,2; Jin, Long 1,2; Wang, Yaonan 3; Xiao, Lin 4; Zhang, Jiliang 5
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
	2020-09-01
卷号	31 期号:9 页码:3555-3569
通讯作者	Jin, Long(longjin@ieee.org)
摘要	It has been reported that some specially designed recurrent neural networks and their related neural dynamics are efficient for solving quadratic programming (QP) problems in the real domain. A complex-valued QP problem is generated if its variable vector is composed of the magnitude and phase information, which is often depicted in a time-dependent form. Given the important role that complex-valued problems play in cybernetics and engineering, computational models with high accuracy and strong robustness are urgently needed, especially for time-dependent problems. However, the research on the online solution of time-dependent complex-valued problems has been much less investigated compared to time-dependent real-valued problems. In this article, to solve the online time-dependent complex-valued QP problems subject to linear constraints, two new discrete-time neural dynamics models, which can achieve global convergence performance in the presence of perturbations with the provided theoretical analyses, are proposed and investigated. In addition, the second proposed model is developed to eliminate the operation of explicit matrix inversion by introducing the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method. Moreover, computer simulation results and applications in robotics and filters are provided to illustrate the feasibility and superiority of the proposed models in comparison with the existing solutions.
关键词	Computational modeling Convergence Mathematical model Recurrent neural networks Perturbation methods Robots Numerical models Complex domain discrete-time neural dynamics (DTND) quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) quadratic programming (QP)
DOI	10.1109/TNNLS.2019.2944992
关键词[WOS]	REDUNDANT MANIPULATORS ; NETWORK
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2017YFE0118900] ; National Natural Science Foundation of China[61703189] ; Natural Science Foundation of Gansu Province, China[18JR3RA264] ; Sichuan Science and Technology Program[19YYJC1656] ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences[20190112] ; Fundamental Research Funds for the Central Universities[lzujbky-2019-89]
项目资助者	National Key R&D Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Gansu Province, China ; Sichuan Science and Technology Program ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities
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:000566342500032
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：77[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/173211/41947
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Jin, Long
作者单位	1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China 4.Hunan Normal Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China 5.Univ Sheffield, Dept Elect & Elect Engn, Sheffield S10 2TN, S Yorkshire, England
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Qi, Yimeng,Jin, Long,Wang, Yaonan,et al. Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(9):3555-3569.
APA	Qi, Yimeng,Jin, Long,Wang, Yaonan,Xiao, Lin,&Zhang, Jiliang.(2020).Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(9),3555-3569.
MLA	Qi, Yimeng,et al."Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.9(2020):3555-3569.