重型车辆Nox排放量的传统预测方法中，由于没有利用误差反馈来修正预测算法，导致预测出的Nox排放量数据与真实数据相差较大。为此提出基于智能算法的重型车辆 Nox排放量预测方法。收集大量的重型车辆真实的Nox排放数据，计算出重型车辆Nox综合排放因子，基于智能算法构建 BP 人工神经网络，设置隐含模式的工作层，输入收集到的真实数据对智能算法进行训练，利用BP神经网络误差反传的功能对每个数据对进行训练，通过误差反馈修正算法的预测误差。设计道路预测实验，将基于智能算法得出的重型车辆 Nox排放量数据与真实排放量数据进行对比，得出两者数据结果高度一致的结论，表明该方法的预测数据误差较小，能够进行有效的重型车辆Nox排放量预测。
A Prediction Method for NOx emissions from Heavy-duty Vehicles Based on Intelligent Algorithms
In the traditional prediction method of NOx emissions from heavy-duty vehicles,the lack of error feedback to correct the prediction algorithm results in significant discrepancies between the predicted NOx emis-sions data and the actual data.A method for predicting NOx emissions from heavy-duty vehicles based on intelli-gent algorithms is proposed.Collect a large amount of real NOx emission data from heavy-duty vehicles,calcu-late the comprehensive NOx emission factor of heavy-duty vehicles,construct a BP artificial neural network based on intelligent algorithms,set a hidden mode working layer,input the collected real data to train the intelli-gent algorithm,use the error backpropagation function of the BP neural network to train each data pair,and cor-rect the prediction error of the algorithm through error feedback.Design a road prediction experiment to compare the Nox emission data of heavy-duty vehicles obtained based on intelligent algorithms with the actual emission data.The conclusion is that the two data results are highly consistent,indicating that the prediction data error of this method is small and can effectively predict the Nox emission of heavy-duty vehicles.
NETL
NSTL
万方数据
