According to the World Health Organization, air pollution with PM₁₀ and PM_2.5 (PM-particulate matter) is a significant problem that can have serious consequences for human health. Vehicles, as one of the main sources of PM₁₀ and PM_2.5 emissions, pollute the air and the environment both by creating particles by burning fuel in the engine, and by wearing of various elements in some vehicle systems. In this paper, the authors conducted the prediction of the formation of PM₁₀ and PM_2.5 particles generated by the wear of the braking system using a neural network (Artificial Neural Networks (ANN)). In this case, the neural network model was created based on the generated particles that were measured experimentally, while the validity of the created neural network was checked by means of a comparative analysis of the experimentally measured amount of particles and the prediction results. The experimental results were obtained by testing on an inertial braking dynamometer, where braking was performed in several modes, that is under different braking parameters (simulated vehicle speed, brake system pressure, temperature, braking time, braking torque). During braking, the concentration of PM₁₀ and PM_2.5 particles was measured simultaneously. The total of 196 measurements were performed and these data were used for training, validation, and verification of the neural network. When it comes to simulation, a comparison of two types of neural networks was performed with one output and with two outputs. For each type, network training was conducted using three different algorithms of backpropagation methods. For each neural network, a comparison of the obtained experimental and simulation results was performed. More accurate prediction results were obtained by the single-output neural network for both particulate sizes, while the smallest error was found in the case of a trained neural network using the Levenberg-Marquardt backward propagation algorithm. The aim of creating such a prediction model is to prove that by using neural networks it is possible to predict the emission of particles generated by brake wear, which can be further used for modern traffic systems such as traffic control. In addition, this wear algorithm could be applied on other vehicle systems, such as a clutch or tires.

据世界卫生组织称，PM ₁₀和 PM _2.5（PM 颗粒物）造成的空气污染是一个严重问题，可能对人类健康造成严重后果。车辆作为 PM ₁₀和 PM _2.5排放的主要来源之一，通过在发动机中燃烧燃料产生颗粒以及在某些车辆系统中磨损各种元素来污染空气和环境。在本文中，作者对 PM ₁₀和 PM _2.5的形成进行了预测使用神经网络（人工神经网络（ANN））制动系统磨损产生的颗粒。在这种情况下，基于实验测量的生成粒子创建神经网络模型，同时通过对实验测量的粒子量和预测结果的比较分析来检查创建的神经网络的有效性。实验结果是通过在惯性制动测功机上进行测试获得的，其中制动在多种模式下进行，即在不同的制动参数（模拟车速、制动系统压力、温度、制动时间、制动扭矩）下。刹车时，PM ₁₀和 PM _2.5的浓度同时测量颗粒。总共进行了 196 次测量，这些数据用于训练、验证和验证神经网络。在模拟方面，对两种类型的神经网络进行了一个输出和两个输出的比较。对于每种类型，使用三种不同的反向传播算法进行网络训练。对于每个神经网络，对获得的实验和模拟结果进行了比较。对于两种颗粒尺寸，单输出神经网络都获得了更准确的预测结果，而在使用 Levenberg-Marquardt 反向传播算法训练的神经网络的情况下，发现了最小的误差。创建这样一个预测模型的目的是证明通过使用神经网络可以预测刹车磨损产生的粒子的排放，这可以进一步用于现代交通系统，如交通控制。此外，这种磨损算法可以应用于其他车辆系统，例如离合器或轮胎。