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首页> 外文期刊>Industrial Informatics, IEEE Transactions on >Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System
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Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System

机译：多层神经网络的Levenberg–Marquardt反向传播训练，用于评估安全关键的网络物理系统的状态

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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.
机译：作为重要的安全关键型物理网络系统（CPS），制动系统对于电动汽车的安全运行至关重要。准确估计制动压力对于汽车CPS设计和控制非常重要。本文基于Levenberg-Marquardt反向传播（LMBP）训练算法，基于多层人工神经网络（ANN），开发了一种新型的电动汽车制动压力概率估计方法。首先，说明了用于制动压力估计的多层人工神经网络的高级体系结构。然后，介绍了用于训练前馈神经网络（FFNN）的标准反向传播（BP）算法。基于BP的基本概念，提出了一种更有效的LMBP训练算法。接下来，在底盘测功机上以标准行驶周期进行真实的车辆测试。收集车辆和动力总成系统的实验数据，并选择用于FFNN训练收集的特征向量。最后，使用测得的车辆数据对开发的多层ANN进行训练，并评估制动压力估计的性能并将其与其他可用的学习方法进行比较。实验结果验证了在实际减速情况下基于ANN的制动压力估计方法的可行性和准确性。

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