基于宽度学习系统的仓储粮情风险点预测模型

Risk point prediction model of warehouse grain situation based on broad learning system

及时的仓储粮情预测是保证储粮安全的必要手段。目前,传统预测方法多从某一侧面对仓储粮情进行预测,无法实现对仓储粮情风险的精准综合评估,而深度学习方法则存在着所需训练样本数量巨大、训练难度高、时间长等瓶颈问题。针对这一现状,采用基于宽度学习的特征提取与融合方法,以及基于增量学习的训练方法(增强节点和输入数据增量算法),结合粮情数据的多模态特征,在宽度学习系统现有框架的基础上,提出了基于宽度学习系统的粮情风险预测模型。结果表明,与现有深度学习模型相比,在不降低预测准确度的前提下,预测模型大大节省了模型训练时间,降低了训练难度。预测模型成为深度学习模型的一种有效替代方案。

Timely forecast of stored grain condition is a necessary means to ensure the safety of stored grain.At present,traditional forecasting methods mostly predict the stored grain situation from one side,and cannot achieve accurate comprehensive assessment of the risk of stored grain situation.However,deep learning methods have bottleneck problems such as a large number of required training samples,high training difficulty and long training time.In view of this situation,by using the feature extraction and fusion method based on broad learning and the training method based on incremental learning,and combined with the multi-modal characteristics of grain situation data,a grain situation risk prediction model was proposed on the basis of the existing framework of the broad learning system.The results showed that,compared with the existing deep learning model,the training difficulty and time-consuming of the model were greatly reduced without reducing the accuracy of prediction.The predictive model proposed in the paper may be an effective alternative to deep learning models.