Levenberg-Marquadt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System

doi:10.1109/TII.2017.2777460

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

	Levenberg-Marquadt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System
	Lv, Chen 1; Xing, Yang 1; Zhang, Junzhi 2; Na, Xiaoxiang 3; Li, Yutong 2; Liu, Teng 4; Cao, Dongpu 1; Wang, Fei-Yue4
发表期刊	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
	2018-08-01
卷号	14 期号:8 页码:3436-3446
文章类型	Article
摘要	As an important safety-critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer artificial neural networks (ANNs) with Levenberg-Marquardt backpropagation (LMBP) training algorithm. First, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of BP, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.
关键词	Artificial Neural Networks (Anns) Brake Pressure Estimation Cyber-physical System (Cps) Electric Vehicle (Ev) Levenberg-marquardt Backpropagation (Lmbp) Safety-critical System
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TII.2017.2777460
关键词[WOS]	REGENERATIVE BRAKING ; ELECTRIC VEHICLE ; POWERTRAIN ; ALGORITHM
收录类别	SCI
语种	英语
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS记录号	WOS:000441446300014
引用统计	被引频次：224[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/173211/21842
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
作者单位	1.Cranfield Univ, Adv Vehicle Engn Ctr, Cranfield MK43 0AL, Beds, England 2.Tsinghua Univ, Dept Automot Engn, Beijing 100084, Peoples R China 3.Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Lv, Chen,Xing, Yang,Zhang, Junzhi,et al. Levenberg-Marquadt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2018,14(8):3436-3446.
APA	Lv, Chen.,Xing, Yang.,Zhang, Junzhi.,Na, Xiaoxiang.,Li, Yutong.,...&Wang, Fei-Yue.(2018).Levenberg-Marquadt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,14(8),3436-3446.
MLA	Lv, Chen,et al."Levenberg-Marquadt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 14.8(2018):3436-3446.