The production data in the industrialfield have the characteristics of multimodality,high dimensionality and large correlation differences between attributes.Existing data prediction methods cannot effectively capture ...The production data in the industrialfield have the characteristics of multimodality,high dimensionality and large correlation differences between attributes.Existing data prediction methods cannot effectively capture time series and modal features,which leads to prediction hysteresis and poor prediction stabil-ity.Aiming at the above problems,this paper proposes a time-series and modal fea-tureenhancementmethodbasedonadual-stageself-attentionmechanism(DATT),and a time series prediction method based on a gated feedforward recurrent unit(GFRU).On this basis,the DATT-GFRU neural network with a gated feedforward recurrent neural network and dual-stage self-attention mechanism is designed and implemented.Experiments show that the prediction effect of the neural network prediction model based on DATT is significantly improved.Compared with the traditional prediction model,the DATT-GFRU neural network has a smaller aver-age error of model prediction results,stable prediction performance,and strong generalization ability on the three datasets with different numbers of attributes and different training sample sizes.展开更多
基金This work is financially supported by:The National Key R&D Program of China(No.2020YFB1712600)The Fundamental Research Funds for Central University(No.3072022QBZ0601)+1 种基金The National Natural Science Foundation of China(No.62272126)The National Natural Science Foundation of China(No.61872104).
文摘The production data in the industrialfield have the characteristics of multimodality,high dimensionality and large correlation differences between attributes.Existing data prediction methods cannot effectively capture time series and modal features,which leads to prediction hysteresis and poor prediction stabil-ity.Aiming at the above problems,this paper proposes a time-series and modal fea-tureenhancementmethodbasedonadual-stageself-attentionmechanism(DATT),and a time series prediction method based on a gated feedforward recurrent unit(GFRU).On this basis,the DATT-GFRU neural network with a gated feedforward recurrent neural network and dual-stage self-attention mechanism is designed and implemented.Experiments show that the prediction effect of the neural network prediction model based on DATT is significantly improved.Compared with the traditional prediction model,the DATT-GFRU neural network has a smaller aver-age error of model prediction results,stable prediction performance,and strong generalization ability on the three datasets with different numbers of attributes and different training sample sizes.
