基于深度学习的线上农产品销量预测模型研究

发布时间：2018-02-11 04:17

  本文关键词： 深度学习 农产品销量预测 农产品销量评价指标 ICM　出处：《计算机应用研究》2017年08期 　论文类型：期刊论文

【摘要】：针对线上农产品销售存在的信息不对称问题,提出一种结合深度学习算法优势和涉农电商销售数据特点的皇冠模型(ICM)。首先建立因素评价指标,将销量分为四个类别;其次采用两层自编码网络提取样本特征,并生成新的特征向量;然后利用带标签样本集训练分类器并对无标签训练样本分类;最后利用BP微调整个网络参数得到使损失函数值达到最小的最优参数,实现线上农产品的销量分类预测。经仿真分析,验证了ICM的分类准确率高达88%,明显高于其他未将数据进行特征学习的浅层分类器,证明了ICM具有较好的增量自学习能力和层次认知能力。
[Abstract]:Aiming at the problem of information asymmetry existing in online agricultural product sales, a crown model combining the advantages of depth learning algorithm and the characteristics of sales data of agribusiness is proposed. Firstly, the evaluation index of factors is established, and the sales volume is divided into four categories. Secondly, a two-layer self-coding network is used to extract the sample features and generate a new feature vector, and then the untagged training samples are classified by using the labeled sample set to train the classifier and to classify the untagged training samples. Finally, the optimal parameters of the loss function are obtained by fine-tuning the whole network parameters, and the classification and prediction of the sales volume of agricultural products on line are realized. It is verified that the classification accuracy of ICM is as high as 88%, which is obviously higher than that of other shallow classifiers without feature learning. It is proved that ICM has better incremental self-learning ability and hierarchical cognitive ability.
【作者单位】： 河北工业大学计算机科学与软件学院;河北省大数据计算重点实验室;河北工业大学经济管理学院;
【基金】：天津市软科学基金项目(16450303D) 河北省社会科学基金资助项目(HB15GL112) 河北省科技计划资助项目(16450303D)
【分类号】：F323.7;F724.6;TP181
，

本文编号：1502187