Research on Intake Flow Prediction of Gasoline Engine under Transient Conditions Based on SSA-GRNN
To address the challenge of predicting gasoline engine intake flow under transition conditions,a prediction model based on a Sparrow Search Algorithm(SSA)optimized Generalized Regression Neural Network(GRNN)is developed.The model employs the SSA algorithm to optimize the smoothing factor of GRNN by identifying the best value and extracting feature parameters using the Spearman method and comparison analysis method to obtain improved prediction accuracy and generalization performance.The model is trained and tested using sample data of intake flow under transition conditions.The results show that the average relative errors of the predicted values of the SSA-GRNN model for acceleration and deceleration conditions are less than 1%.Compared with BP,RBF and GA-SVR intake flow prediction models,the SSA-GRNN model demonstrates higher prediction accuracy and generalization performance,making it more suitable for predicting gasoline engine intake flow under transition conditions.
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