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
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河南工業(yè)大學信息科學與工程學院 鄭州 450001
Multi-index Prediction Model of Wheat Quality Based on Long Short-Term Memory and Generative Adversarial Network
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College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
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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。
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.
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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) 16.00 30.50 23.18 4.24 降落數值(s) 365.00 630.00 482.81 69.36 沉降值(ml) 19.50 62.00 40.11 13.94 發(fā)芽率(%) 0 97.00 71.29 28.96 過氧化物酶(U/g) 1400.00 4100.00 3171.35 667.93 電導率(μs/(cm·g)) 25.50 60.50 39.11 8.75 
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表 2 模型不同訓練窗口長度誤差對比
窗口長度 2 4 6 8 脂肪酸值 0.260 0.258 0.308 0.328 降落數值 0.325 0.263 0.228 0.277 沉降值 0.356 0.447 0.336 0.407 發(fā)芽率 0.652 0.530 0.483 0.511 過氧化物酶 0.424 0.455 0.402 0.415 電導率 0.412 0.324 0.329 0.374
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表 3 LSTM-GAN模型不同結構參數訓練誤差
隱含層層數 2 3 5 神經元個數 6 8 10 12 6 8 10 12 6 8 10 12 脂肪酸值 0.285 0.245 0.275 0.281 0.265 0.290 0.260 0.285 0.255 0.355 0.345 0.335 降落數值 0.295 0.265 0.305 0.335 0.315 0.235 0.300 0.342 0.335 0.315 0.335 0.355 沉降值 0.400 0.405 0.410 0.427 0.405 0.425 0.435 0.533 0.445 0.540 0.315 0.493 發(fā)芽率 0.505 0.560 0.488 0.494 0.610 0.570 0.532 0.582 0.635 0.623 0.657 0.625 過氧化物酶 0.365 0.345 0.340 0.342 0.370 0.280 0.300 0.369 0.325 0.380 0.415 0.409 電導率 0.330 0.370 0.340 0.404 0.440 0.375 0.425 0.417 0.555 0.370 0.435 0.454 綜合誤差 2.180 2.190 2.158 2.284 2.405 2.175 2.252 2.528 2.550 2.583 2.502 2.671
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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 0.275 0.295 0.315 降落數值 0.305 0.290 0.255 沉降值 0.360 0.320 0.245 發(fā)芽率 0.422 0.419 0.428 過氧化物酶 0.390 0.350 0.365 電導率 0.290 0.300 0.335
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表 5 不同模型預測誤差對比
LSTM-GAN LSTM 線性回歸 SVR ANN GM 脂肪酸值 0.275 0.285 0.290 0.303 0.326 0.386 降落數值 0.305 0.329 0.577 0.405 0.402 0.511 沉降值 0.410 0.482 0.563 0.366 0.459 0.498 發(fā)芽率 0.488 0.553 0.611 0.467 0.466 0.559 過氧化物酶 0.340 0.378 0.604 0.469 0.460 0.452 電導率 0.340 0.364 0.331 0.372 0.373 0.413 綜合誤差 2.158 2.391 2.976 2.381 2.484 2.817
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