基于频域门控Transformer的润叶过程参数预测方法OA

[Objective]To address the challenges of multivariate interactions,non-stationarity,and time delays in predicting outlet moisture during the hot air leaf conditioning process,this paper proposes ADGformer,a prediction model based on an adaptive frequency domain gated Transformer.[Methods]This model utilizes the fast Fourier transform to capture the periodic components of the time series.It incorporates an attention mechanism enhanced by frequency gating and introduces an adaptive frequency selection mechanism to extract key frequencies and filter out noise.[Results]The model achieves average relative reductions of 7.30%in mean squared error(MSE)and 7.26%in mean absolute error(MAE)across four prediction horizons,demonstrating superior robustness in long-term forecasting tasks.Furthermore,the single-sample inference latency is only 1.9 ms and the industrial qualification rate reaches 92.8%,which indicates a significant accuracy improvement in moisture prediction for the hot air leaf conditioning process.[Conclusion]The proposed ADGformer model provides an effective solution for the fine-grained control of the tobacco leaf conditioning process and offers new theoretical support for complex time-series prediction problems.