Levenberg-Marquadt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System
文献类型：期刊论文
| 作者 | Lv, Chen1; Xing, Yang1; Zhang, Junzhi2; Na, Xiaoxiang3; Li, Yutong2; Liu, Teng4; Cao, Dongpu1; Wang, Fei-Yue4
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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| 出版日期 | 2018-08-01 |
| 卷号 | 14期号:8页码:3436-3446 |
| 关键词 | Artificial Neural Networks (Anns) Brake Pressure Estimation Cyber-physical System (Cps) Electric Vehicle (Ev) Levenberg-marquardt Backpropagation (Lmbp) Safety-critical System |
| DOI | 10.1109/TII.2017.2777460 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | REGENERATIVE BRAKING ; ELECTRIC VEHICLE ; POWERTRAIN ; ALGORITHM |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000441446300014 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21842]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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