基于改进的特征增强型宽度学习的储粮温度预测模型

    Predicting model of stored-grain temperature based on an improved feature enhanced broad learning

    • 摘要: 储粮温度预测是保障粮食安全藏储的重要措施,传统的机器学习模型受限于精度不足,而深度学习方法精度高但存在训练时间长的问题,基于此提出了一种改进的特征增强型宽度学习预测模型。该模型充分利用宽度学习训练速度优势,并利用长短期记忆网络增强模型对储粮数据的时序特征学习能力,再结合多头自注意力机制,增强多维特征的重要相关性信息聚焦,对储粮温度进行预测试验。结果表明:基于部分真实的储粮数据验证了改进的特征增强型宽度学习的储粮温度预测精度比未改进宽度学习更高,同时与卷积-长短期记忆网络、Transformer深度学习模型相比,该模型精度更高、训练时长也更少。本研究结果为安全储粮在技术上提供一种有效的可选方案。

       

      Abstract: The prediction of grain storage temperature is an important measure to ensure the safety of grain storage. The traditional machine learning model is limited by insufficient accuracy, and the deep learning method has high accuracy but long training time. The model makes full use of the advantage of broad learning training speed, and uses long short-term memory network to enhance the learning ability of the model for the time series characteristics of grain storage data. Combined with the multi-head self-attention mechanism, the important correlation information focusing of multi-dimensional features is enhanced, and the prediction test of grain storage temperature is carried out. The results show that the grain storage temperature prediction accuracy of the improved feature-enhanced breadth learning is higher than that of the unimproved broad learning based on some real grain storage data. At the same time, compared with the convolution-long short-term memory network and Transformer deep learning model, the proposed broad model has higher accuracy and less training time. The results of this study provide an effective alternative scheme for safe grain storage in technology.

       

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