Complex-Valued Discrete-Time Neural Dynamics for Perturbed Time-Dependent Complex Quadratic Programming With Applications
文献类型：期刊论文
| 作者 | Qi, Yimeng2,3; Jin, Long2,3; Wang, Yaonan1; Xiao, Lin5; Zhang, Jiliang4 |
| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 出版日期 | 2020-09-01 |
| 卷号 | 31期号:9页码:3555-3569 |
| 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) |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2019.2944992 |
| 通讯作者 | Jin, Long(longjin@ieee.org) |
| 英文摘要 | 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. |
| WOS关键词 | REDUNDANT MANIPULATORS ; NETWORK |
| 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] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000566342500032 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/41946]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Jin, Long |
| 作者单位 | 1.Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China 2.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.Univ Sheffield, Dept Elect & Elect Engn, Sheffield S10 2TN, S Yorkshire, England 5.Hunan Normal Univ, Coll Comp Sci & Elect Engn, Changsha 410082, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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