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
2018-08-01
发表期刊IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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
DOI10.1109/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:5[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
专题复杂系统管理与控制国家重点实验室_先进控制与自动化
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.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Lv, Chen]的文章
[Xing, Yang]的文章
[Zhang, Junzhi]的文章
百度学术
百度学术中相似的文章
[Lv, Chen]的文章
[Xing, Yang]的文章
[Zhang, Junzhi]的文章
必应学术
必应学术中相似的文章
[Lv, Chen]的文章
[Xing, Yang]的文章
[Zhang, Junzhi]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。