针对目前汽油机进气流量预测精度不高的问题，分析支持向量回归机(SVR)应用在进气流量预测的可行性，提出一种基于SVR的进气流量预测模型。该模型通过结合支持向量回归机的结构优势，采用灰色关联分析法(GRA)对模型的特征向量进行提取，并利用遗传算法(GA)对模型参数进行寻优辨识，以提高模型的泛化性能和预测精度。运用汽油机过渡工况仿真试验数据对模型进行了训练和预测，并应用MATLAB/LIBSVM工具箱实现SVR模型的回归预测功能。结果表明：SVR模型的预测值与试验值的误差控制在2％范围之内，有效实现了过渡工况进气流量的预测；与常规的RBF神经网络预测模型、BP神经网络预测模型相比，SVR模型具有更高的预测精度，适用于汽油机过渡工况空燃比的精准控制。

In view of the low accuracy of gasoline engine intake flow prediction, the feasibility of support vector regression (SVR) to the intake flow prediction was analyzed, and an intake flow prediction model based on SVR was put forward. Based on the structural advantages of SVR, grey correlation analysis (GRA) was used to extract the feature vector of model, genetic algorithm (GA) was used to optimize and identify the model parameters, and the generalization performance and prediction accuracy of model hence improved. The model was trained and predicted by using the simulation test data of gasoline engine transient conditions, and the regression prediction function of SVR model was realized by using MATLAB/LIBSVM toolbox. The results show that the error between the predicted value of SVR model and the test value is within 2% and the prediction of inlet flow under the transient condition is effectively realized; Compared with conventional RBF neural network prediction model and BP neural network prediction model, it has higher prediction accuracy and is suitable for precise control of air-fuel ratio of gasoline engine under transient conditions.