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基于长短期记忆生成对抗网络的小麦品质多指标预测模型

蒋华伟 张磊

蒋华伟, 张磊. 基于长短期记忆生成对抗网络的小麦品质多指标预测模型[J]. 电子与信息学报, 2020, 42(12): 2865-2872. doi: 10.11999/JEIT190802
引用本文: 蒋华伟, 张磊. 基于长短期记忆生成对抗网络的小麦品质多指标预测模型[J]. 电子与信息学报, 2020, 42(12): 2865-2872. doi: 10.11999/JEIT190802
Huawei JIANG, Lei ZHANG. Multi-index Prediction Model of Wheat Quality Based on Long Short-Term Memory and Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2020, 42(12): 2865-2872. doi: 10.11999/JEIT190802
Citation: Huawei JIANG, Lei ZHANG. Multi-index Prediction Model of Wheat Quality Based on Long Short-Term Memory and Generative Adversarial Network[J]. Journal of Electronics & Information Technology, 2020, 42(12): 2865-2872. doi: 10.11999/JEIT190802

基于长短期记忆生成对抗网络的小麦品质多指标预测模型

doi: 10.11999/JEIT190802
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

Multi-index Prediction Model of Wheat Quality Based on Long Short-Term Memory and Generative Adversarial Network

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:

    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。

  • 图  1  长短期记忆网络单元结构

    图  2  长短期记忆生成对抗网络

    图  3  强筋麦多指标预测结果

    表  1  小麦多指标数据集统计信息

    最小值最大值均值标准差
    脂肪酸值(mgKOH/100 g)16.0030.5023.184.24
    降落数值(s)365.00630.00482.8169.36
    沉降值(ml)19.5062.0040.1113.94
    发芽率(%)097.0071.2928.96
    过氧化物酶(U/g)1400.004100.003171.35667.93
    电导率(μs/(cm·g))25.5060.5039.118.75
    下载: 导出CSV

    表  2  模型不同训练窗口长度误差对比

    窗口长度2468
    脂肪酸值0.2600.2580.3080.328
    降落数值0.3250.2630.2280.277
    沉降值0.3560.4470.3360.407
    发芽率0.6520.5300.4830.511
    过氧化物酶0.4240.4550.4020.415
    电导率0.4120.3240.3290.374
    下载: 导出CSV

    表  3  LSTM-GAN模型不同结构参数训练误差

    隐含层层数235
    神经元个数681012681012681012
    脂肪酸值0.2850.2450.2750.2810.2650.2900.2600.2850.2550.3550.3450.335
    降落数值0.2950.2650.3050.3350.3150.2350.3000.3420.3350.3150.3350.355
    沉降值0.4000.4050.4100.4270.4050.4250.4350.5330.4450.5400.3150.493
    发芽率0.5050.5600.4880.4940.6100.5700.5320.5820.6350.6230.6570.625
    过氧化物酶0.3650.3450.3400.3420.3700.2800.3000.3690.3250.3800.4150.409
    电导率0.3300.3700.3400.4040.4400.3750.4250.4170.5550.3700.4350.454
    综合误差2.1802.1902.1582.2842.4052.1752.2522.5282.5502.5832.5022.671
    下载: 导出CSV

    表  4  不同筋力小麦多指标预测误差对比

    强筋中筋弱筋
    脂肪酸值0.2750.2950.315
    降落数值0.3050.2900.255
    沉降值0.3600.3200.245
    发芽率0.4220.4190.428
    过氧化物酶0.3900.3500.365
    电导率0.2900.3000.335
    下载: 导出CSV

    表  5  不同模型预测误差对比

    LSTM-GANLSTM线性回归SVRANNGM
    脂肪酸值0.2750.2850.2900.3030.3260.386
    降落数值0.3050.3290.5770.4050.4020.511
    沉降值0.4100.4820.5630.3660.4590.498
    发芽率0.4880.5530.6110.4670.4660.559
    过氧化物酶0.3400.3780.6040.4690.4600.452
    电导率0.3400.3640.3310.3720.3730.413
    综合误差2.1582.3912.9762.3812.4842.817
    下载: 导出CSV
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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

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