Prediction model for NOx emission of starting process of the diesel engine based on online learning
In order to build an accurate NOx emission prediction model for diesel engine starting process,the transient operation characteristics of the starting process is analyzed,a nonlinear autoregressive neural network is used to select model features based on starting test data.The prediction performance of the self-attention mechanism model and the standard backpropagation neural network model for NOx emissions is compared,it is found that the self-attention mechanism model has good prediction performance and small root mean square error.Comparing the predictive performance of self attention mechanism models in two online learning methods,namely online gradient descent algorithm and FTRL algorithm,it is found that adopting FTRL algorithm could improve the predictive performance of the model under unknown working conditions.Comparing the predictive performance of the self attention mechanism model before and after learning,it is found that the root mean square error of the learned model increases,but the predictive ability is good.Comparing the prediction performance of online learning models and full data training set offline learning models,it is found that the coefficient of determination and root mean square error of both models are not significantly different,and the prediction performance is good.However,the amount of data and training time used in online learning have been respectively reduced by 68.7％and 73.6％,significantly reducing storage and training costs.The results indicate that the self-attention mechanism model using FTRL algorithm could reduce data storage and training resource costs,and provide real-time feedback on prediction requirements.
NETL
NSTL
万方数据
