Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition

doi:10.1109/TSMC.2021.3114213

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition
	Jin, Long 1; Li, Jiachang 1; Sun, Zhongbo 2; Lu, Jingwei3,4 ; Wang, Fei-Yue3,4
发表期刊	IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN	2168-2216
	2021-10-01
页码	9
通讯作者	Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
摘要	Many complex nonlinear optimization or control issues can be transformed into the solving of time-varying nonlinear equations (TVNEs), playing a fundamental role in the control and management of complex systems. As a result, a robust and high-precision online solution method is significant for TVNE. However, there are three main challenges for handling TVNE via the existing methods: First, short-time invariance assumption frequently leveraged in the existing methods leads to lagging errors that are difficult to eliminate. Second, it is difficult in dealing with unknown noise disturbance during the solution process, which causes low solution accuracy or solution failure. Third, existing continuous-time methods are hard to be implemented on digital equipments. In this article, an anti-noise discrete-time neural dynamics (DTND) is designed and studied to overcome the above issues systematically. The theoretical analysis and numerical simulations demonstrate that the proposed model effectively eliminates the lagging errors and achieves the accurate solution of the TVNE in a noisy environment. Moreover, to verify the superior numerical computational property of the DTND model, the intention recognition of lower limbs is explored from the artificial systems, computational experiments, and parallel execution (ACP) framework. Specifically, a nonlinear artificial dynamic system (NADS) concerning the human surface electromyogram (sEMG) signals and joint information is established, which performs in parallel with the actual human lower limb physical experiments. Simulation results illustrate that, within the acceptable range of the digital computer, the controller designed by the DTND model can well guide the NADS to accurately recognize the motion intention of the human lower limb.
关键词	Mathematical models Computational modeling Numerical models Nonlinear equations Convergence Analytical models Time-varying systems Artificial systems computational experiments and parallel execution (ACP) discrete-time neural dynamics (DTND) model human lower limb physical experiment nonlinear equations
DOI	10.1109/TSMC.2021.3114213
关键词[WOS]	MUSCLE FORCES ; JOINT MOMENTS ; SYSTEMS ; NETWORK
收录类别	SCI
语种	英语
资助项目	Key-Area Research and Development Program of Guangdong Province[2020B0909050003] ; Key Research and Development Program 2020 of Guangzhou[202007050002] ; National Natural Science Foundation of China[U1811463] ; National Natural Science Foundation of China[62176109] ; National Natural Science Foundation of China[61873304] ; Team Project of Natural Science Foundation of Qinghai Province (China)[2020-ZJ-903] ; Natural Science Foundation of Gansu Province[21JR7RA531] ; Natural Science Foundation of Gansu Province[20JR10RA639] ; CAAI-Huawei MindSpore Open Fund[CAAIXSJLJJ-2020-009A] ; China Postdoctoral Science Foundation[2018M641784] ; Key Science and Technology Projects of Jilin Province[20200201291JC] ; Chongqing Key Laboratory of Mobile Communications Technology[cqupt-mct-202004] ; Fundamental Research Funds for the Central Universities[lzujbky-2021-65] ; Gansu Provincial Youth Doctoral Fund of Colleges and Universities[2021QB-003] ; Science and Technology Plan Project of Chengguan District (Lanzhou)[2021-1-2] ; Science and Technology Plan Project of Chengguan District (Lanzhou)[2021-7-1]
项目资助者	Key-Area Research and Development Program of Guangdong Province ; Key Research and Development Program 2020 of Guangzhou ; National Natural Science Foundation of China ; Team Project of Natural Science Foundation of Qinghai Province (China) ; Natural Science Foundation of Gansu Province ; CAAI-Huawei MindSpore Open Fund ; China Postdoctoral Science Foundation ; Key Science and Technology Projects of Jilin Province ; Chongqing Key Laboratory of Mobile Communications Technology ; Fundamental Research Funds for the Central Universities ; Gansu Provincial Youth Doctoral Fund of Colleges and Universities ; Science and Technology Plan Project of Chengguan District (Lanzhou)
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Cybernetics
WOS记录号	WOS:000732395900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	生物特征识别
引用统计	被引频次：37[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/173211/46996
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
通讯作者	Wang, Fei-Yue
作者单位	1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China 2.Changchun Univ Technol, Dept Control Engn, Changchun 130012, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
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	Jin, Long,Li, Jiachang,Sun, Zhongbo,et al. Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,2021:9.
APA	Jin, Long,Li, Jiachang,Sun, Zhongbo,Lu, Jingwei,&Wang, Fei-Yue.(2021).Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition.IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS,9.
MLA	Jin, Long,et al."Neural Dynamics for Computing Perturbed Nonlinear Equations Applied to ACP-Based Lower Limb Motion Intention Recognition".IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2021):9.