张磊
河南工业大学信息科学与工程学院 郑州 450001
基金项目:国家自然科学基金(51677055)，河南省自然科学基金(162300410055)，河南省高校科技创新团队计划项目(16IRTSTHN026)
详细信息
作者简介:蒋华伟：男，1970年生，博士，教授，博士生导师，研究方向为粮食信息处理
张磊：男，1996年生，硕士生，研究方向为粮食多指标智能预测
通讯作者:蒋华伟 lhwcad@126.com
中图分类号:TP391计量
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被引次数:0
出版历程
收稿日期:2019-10-16
修回日期:2020-10-18
网络出版日期:2020-10-26
刊出日期:2020-12-08
Multi-index Prediction Model of Wheat Quality Based on Long Short-Term Memory and Generative Adversarial Network
Huawei JIANG,,Lei ZHANG
College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
Funds:The National Natural Science Foundation of China (51677055), The Natural Science Foundation of Henan Province (162300410055), The Science and Technology Innovation Team Planning Project of University of Henan Province (16IRTSTHN026)
摘要
摘要:小麦多生理生化指标变化趋势反映了储藏品质的劣变状态，预测多指标时序数据会因关联性及相互作用而产生较大误差，为此该文基于长短期记忆网络(LSTM)和生成式对抗网络(GAN)提出一种改进拓扑结构的长短期记忆生成对抗网络(LSTM-GAN)模型。首先，由LSTM预测多指标不同时序数据的劣变趋势；其次，根据多指标的关联性并结合GAN的对抗学习方法来降低综合预测误差；最后通过优化目标函数及训练模型得出多指标预测结果。经实验分析发现：小麦多指标的长短期时序数据的变化趋势不同，进一步优化模型结构及训练时序长度可有效降低预测结果的误差；特定条件下小麦品质过快劣变会使多指标预测误差增大，因此应充分考虑储藏期环境变化对多指标数据的影响；LSTM-GAN模型的综合误差相对于仅使用LSTM预测降低了9.745%，并低于多种对比模型，这有助于提高小麦品质多指标预测及分析的准确性。
关键词:长短期记忆网络/
生成式对抗网络/
小麦多指标/
预测模型
Abstract:The change trend of multi-index of wheat reflects the deterioration state of storage quality, while the predicted multi-index data will produce large errors due to its correlation and interaction. For this reason, an improved Long Short-Term Memory and Generative Adversarial Network(LSTM-GAN) model is proposed. The deterioration trend of different time series data of multi-index is predicted by Long Short-Term Memory(LSTM) network, and the improved model may reduce comprehensive prediction error by using Generative Adversarial Network(GAN) according to the correlation of multi-index. Finally, the prediction results obtained by optimizing the objective function and model structure. The experimental analysis shows that the training sequence length and structural parameters of the optimization model can effectively reduce the error of the prediction result. The deterioration of wheat quality under certain conditions will increase the prediction error of multi-index. Therefore, the influence of environmental changes during storage period on multi-index data should be fully considered. The comprehensive error of the LSTM-GAN model is reduced by 9.745% compared with the LSTM prediction and lower than multiple comparison models, which can improve the prediction of wheat quality indexes.
Key words:Long Short-Term Memory(LSTM) network/
Generative Adversarial Network(GAN)/
Wheat multi-index/
Prediction model
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