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基于長短期記憶生成對抗網絡的小麥品質多指標預測模型

蔣華偉 張磊

蔣華偉, 張磊. 基于長短期記憶生成對抗網絡的小麥品質多指標預測模型[J]. 電子與信息學報, 2020, 42(12): 2865-2872. doi: 10.11999/JEIT190802
引用本文: 蔣華偉, 張磊. 基于長短期記憶生成對抗網絡的小麥品質多指標預測模型[J]. 電子與信息學報, 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

基于長短期記憶生成對抗網絡的小麥品質多指標預測模型

doi: 10.11999/JEIT190802
基金項目: 國家自然科學基金(51677055),河南省自然科學基金(162300410055),河南省高??萍紕?chuàng)新團隊計劃項目(16IRTSTHN026)
詳細信息
    作者簡介:

    蔣華偉:男,1970年生,博士,教授,博士生導師,研究方向為糧食信息處理

    張磊:男,1996年生,碩士生,研究方向為糧食多指標智能預測

    通訊作者:

    蔣華偉 lhwcad@126.com

  • 中圖分類號: 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:

    小麥多生理生化指標變化趨勢反映了儲藏品質的劣變狀態(tài),預測多指標時序數據會因關聯性及相互作用而產生較大誤差,為此該文基于長短期記憶網絡(LSTM)和生成式對抗網絡(GAN)提出一種改進拓撲結構的長短期記憶生成對抗網絡(LSTM-GAN)模型。首先,由LSTM預測多指標不同時序數據的劣變趨勢;其次,根據多指標的關聯性并結合GAN的對抗學習方法來降低綜合預測誤差;最后通過優(yōu)化目標函數及訓練模型得出多指標預測結果。經實驗分析發(fā)現:小麥多指標的長短期時序數據的變化趨勢不同,進一步優(yōu)化模型結構及訓練時序長度可有效降低預測結果的誤差;特定條件下小麥品質過快劣變會使多指標預測誤差增大,因此應充分考慮儲藏期環(huán)境變化對多指標數據的影響;LSTM-GAN模型的綜合誤差相對于僅使用LSTM預測降低了9.745%,并低于多種對比模型,這有助于提高小麥品質多指標預測及分析的準確性。

  • 圖  1  長短期記憶網絡單元結構

    圖  2  長短期記憶生成對抗網絡

    圖  3  強筋麥多指標預測結果

    表  1  小麥多指標數據集統(tǒng)計信息

    最小值最大值均值標準差
    脂肪酸值(mgKOH/100 g)16.0030.5023.184.24
    降落數值(s)365.00630.00482.8169.36
    沉降值(ml)19.5062.0040.1113.94
    發(fā)芽率(%)097.0071.2928.96
    過氧化物酶(U/g)1400.004100.003171.35667.93
    電導率(μs/(cm·g))25.5060.5039.118.75
    下載: 導出CSV

    表  2  模型不同訓練窗口長度誤差對比

    窗口長度2468
    脂肪酸值0.2600.2580.3080.328
    降落數值0.3250.2630.2280.277
    沉降值0.3560.4470.3360.407
    發(fā)芽率0.6520.5300.4830.511
    過氧化物酶0.4240.4550.4020.415
    電導率0.4120.3240.3290.374
    下載: 導出CSV

    表  3  LSTM-GAN模型不同結構參數訓練誤差

    隱含層層數235
    神經元個數681012681012681012
    脂肪酸值0.2850.2450.2750.2810.2650.2900.2600.2850.2550.3550.3450.335
    降落數值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
    發(fā)芽率0.5050.5600.4880.4940.6100.5700.5320.5820.6350.6230.6570.625
    過氧化物酶0.3650.3450.3400.3420.3700.2800.3000.3690.3250.3800.4150.409
    電導率0.3300.3700.3400.4040.4400.3750.4250.4170.5550.3700.4350.454
    綜合誤差2.1802.1902.1582.2842.4052.1752.2522.5282.5502.5832.5022.671
    下載: 導出CSV

    表  4  不同筋力小麥多指標預測誤差對比

    強筋中筋弱筋
    脂肪酸值0.2750.2950.315
    降落數值0.3050.2900.255
    沉降值0.3600.3200.245
    發(fā)芽率0.4220.4190.428
    過氧化物酶0.3900.3500.365
    電導率0.2900.3000.335
    下載: 導出CSV

    表  5  不同模型預測誤差對比

    LSTM-GANLSTM線性回歸SVRANNGM
    脂肪酸值0.2750.2850.2900.3030.3260.386
    降落數值0.3050.3290.5770.4050.4020.511
    沉降值0.4100.4820.5630.3660.4590.498
    發(fā)芽率0.4880.5530.6110.4670.4660.559
    過氧化物酶0.3400.3780.6040.4690.4600.452
    電導率0.3400.3640.3310.3720.3730.413
    綜合誤差2.1582.3912.9762.3812.4842.817
    下載: 導出CSV
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  • 收稿日期:  2019-10-16
  • 修回日期:  2020-10-18
  • 網絡出版日期:  2020-10-26
  • 刊出日期:  2020-12-08

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