基于多头注意力机制和长短时记忆网络的布匹产量预测模型OA

With the development of China's textile industry,the prediction of fabric production is facing increas-ingly complex challenges.Traditional prediction models cannot effectively capture the impact of different time periods on production forecasts,resulting in inaccurate prediction results and a long model convergence time.To address this problem,a fabric production prediction model(MHAM-LSTM)is proposed based on a combina-tion of a multi-head attention mechanism(MHAM)and a long short-term memory network(LSTM).By intro-ducing the MHAM mechanism,the model can assign different weights to fabric production data in different time periods,thereby effectively screening out time points that have a greater impact on future production fore-casts.The introduction of the MHAM mechanism helps to reduce the training time of the model and improve the accuracy of the prediction.The LSTM network further optimizes the model's performance in time series data and can capture long-term dependencies over a long period of time.Experimental results show that the model with the MHAM mechanism introduced has achieved a significant improvement in prediction accuracy com-pared to the traditional LSTM model,with an R2 coefficient of up to 0.995.In the monthly prediction of textile production,the SE-Bi-LSTM model has the smallest error compared to the traditional BP and RNN models,with an average error of 0.22%,a reduction of 60.71%and 53.19%respectively.