Levenberg-Marquadt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System
Lv, Chen1; Xing, Yang1; Zhang, Junzhi2; Na, Xiaoxiang3; Li, Yutong2; Liu, Teng4; Cao, Dongpu1; Wang, Fei-Yue4
Source PublicationIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
2018-08-01
Volume14Issue:8Pages:3436-3446
SubtypeArticle
AbstractAs 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.
KeywordArtificial Neural Networks (Anns) Brake Pressure Estimation Cyber-physical System (Cps) Electric Vehicle (Ev) Levenberg-marquardt Backpropagation (Lmbp) Safety-critical System
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TII.2017.2777460
WOS KeywordREGENERATIVE BRAKING ; ELECTRIC VEHICLE ; POWERTRAIN ; ALGORITHM
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial
WOS IDWOS:000441446300014
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21842
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Affiliation1.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
Recommended Citation
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.
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