Recurrent Neural Dynamics Models for Perturbed Nonstationary Quadratic Programs: A Control-Theoretical Perspective
文献类型：期刊论文
| 作者 | Qi, Yimeng2; Jin, Long2; Luo, Xin1,3,4 ; Zhou, MengChu5,6
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| 刊名 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| 出版日期 | 2021-01-14 |
| 页码 | 12 |
| 关键词 | Computational modeling Mathematical model Neural networks Control theory Analytical models Real-time systems Numerical models Control-theoretical techniques perturbed nonstationary quadratic program (QP) recurrent neural dynamics robustness theoretical analysis |
| ISSN号 | 2162-237X |
| DOI | 10.1109/TNNLS.2020.3041364 |
| 通讯作者 | Jin, Long(jinlongsysu@foxmail.com) |
| 英文摘要 | Recent decades have witnessed a trend that control-theoretical techniques are widely leveraged in various areas, e.g., design and analysis of computational models. Computational methods can be modeled as a controller and searching the equilibrium point of a dynamical system is identical to solving an algebraic equation. Thus, absorbing mature technologies in control theory and integrating it with neural dynamics models can lead to new achievements. This work makes progress along this direction by applying control-theoretical techniques to construct new recurrent neural dynamics for manipulating a perturbed nonstationary quadratic program (QP) with time-varying parameters considered. Specifically, to break the limitations of existing continuous-time models in handling nonstationary problems, a discrete recurrent neural dynamics model is proposed to robustly deal with noise. This work shows how iterative computational methods for solving nonstationary QP can be revisited, designed, and analyzed in a control framework. A modified Newton iteration model and an improved gradient-based neural dynamics are established by referring to the superior structural technology of the presented recurrent neural dynamics, where the chief breakthrough is their excellent convergence and robustness over the traditional models. Numerical experiments are conducted to show the eminence of the proposed models in solving perturbed nonstationary QP. |
| 资助项目 | National Natural Science Foundation of China[61703189] ; National Natural Science Foundation of China[11561029] ; National Natural Science Foundation of China[61772493] ; Pioneer Hundred Talents Program of the Chinese Academy of Sciences ; National Key Research and Development Program of China[2017YFE0118900] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Natural Science Foundation of Chongqing (China)[cstc2020jcyj-zdxmX0028] ; Research and Development Foundation of Nanchong (China)[20YFZJ0018] ; CAS Light of West China Program ; Chongqing Key Laboratory of Mobile Communications Technology[cqupt-mct-202004] ; Ministry of Science and Higher Education of the Russian Federation as part of World-class Research Center program: Advanced Digital Technologies[075-15-2020903] ; Fundamental Research Funds for the Central Universities[lzujbky-2019-89] ; Fundamental Research Funds for the Central Universities[lzuxxxy-2019-tm11] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000732213500001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/14749]  |
| 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.Univ Chinese Acad Sci, Chongqing Sch, 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, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Chongqing Engn Res Ctr Big Data Applicat Smart Ci, Chongqing Inst Green & Intelligent Technol, Beijing 100049, Peoples R China 5.New Jersey Inst Technol, Dept Elect & Comp Engn, Newark, NJ 07102 USA 6.St Petersburg State Marine Tech Univ, Dept Cyber Phys Syst, St Petersburg 198262, Russia |
| 推荐引用方式 GB/T 7714 | Qi, Yimeng,Jin, Long,Luo, Xin,et al. Recurrent Neural Dynamics Models for Perturbed Nonstationary Quadratic Programs: A Control-Theoretical Perspective[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:12. |
| APA | Qi, Yimeng,Jin, Long,Luo, Xin,&Zhou, MengChu.(2021).Recurrent Neural Dynamics Models for Perturbed Nonstationary Quadratic Programs: A Control-Theoretical Perspective.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12. |
| MLA | Qi, Yimeng,et al."Recurrent Neural Dynamics Models for Perturbed Nonstationary Quadratic Programs: A Control-Theoretical Perspective".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):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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