基于長短期記憶生成對抗網(wǎng)絡(luò)的小麥品質(zhì)多指標(biāo)預(yù)測模型
doi: 10.11999/JEIT190802 cstr: 32379.14.JEIT190802
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河南工業(yè)大學(xué)信息科學(xué)與工程學(xué)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(biāo)變化趨勢反映了儲藏品質(zhì)的劣變狀態(tài)，預(yù)測多指標(biāo)時(shí)序數(shù)據(jù)會因關(guān)聯(lián)性及相互作用而產(chǎn)生較大誤差，為此該文基于長短期記憶網(wǎng)絡(luò)(LSTM)和生成式對抗網(wǎng)絡(luò)(GAN)提出一種改進(jìn)拓?fù)浣Y(jié)構(gòu)的長短期記憶生成對抗網(wǎng)絡(luò)(LSTM-GAN)模型。首先，由LSTM預(yù)測多指標(biāo)不同時(shí)序數(shù)據(jù)的劣變趨勢；其次，根據(jù)多指標(biāo)的關(guān)聯(lián)性并結(jié)合GAN的對抗學(xué)習(xí)方法來降低綜合預(yù)測誤差；最后通過優(yōu)化目標(biāo)函數(shù)及訓(xùn)練模型得出多指標(biāo)預(yù)測結(jié)果。經(jīng)實(shí)驗(yàn)分析發(fā)現(xiàn)：小麥多指標(biāo)的長短期時(shí)序數(shù)據(jù)的變化趨勢不同，進(jìn)一步優(yōu)化模型結(jié)構(gòu)及訓(xùn)練時(shí)序長度可有效降低預(yù)測結(jié)果的誤差；特定條件下小麥品質(zhì)過快劣變會使多指標(biāo)預(yù)測誤差增大，因此應(yīng)充分考慮儲藏期環(huán)境變化對多指標(biāo)數(shù)據(jù)的影響；LSTM-GAN模型的綜合誤差相對于僅使用LSTM預(yù)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(zhì)多指標(biāo)預(yù)測及分析的準(zhǔn)確性。
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關(guān)鍵詞:
- 長短期記憶網(wǎng)絡(luò) /
- 生成式對抗網(wǎng)絡(luò) /
- 小麥多指標(biāo) /
- 預(yù)測模型
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.
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表 1 小麥多指標(biāo)數(shù)據(jù)集統(tǒng)計(jì)信息
最小值 最大值 均值 標(biāo)準(zhǔn)差 脂肪酸值(mgKOH/100 g) 16.00 30.50 23.18 4.24 降落數(shù)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(dǎo)率(μs/(cm·g)) 25.50 60.50 39.11 8.75 
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表 2 模型不同訓(xùn)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(shù)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(dǎo)率 0.412 0.324 0.329 0.374
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表 3 LSTM-GAN模型不同結(jié)構(gòu)參數(shù)訓(xùn)練誤差
隱含層層數(shù) 2 3 5 神經(jīng)元個(gè)數(shù) 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(shù)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(dǎo)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(biāo)預(yù)測誤差對比
強(qiáng)筋 中筋 弱筋 脂肪酸值 0.275 0.295 0.315 降落數(shù)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(dǎo)率 0.290 0.300 0.335
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表 5 不同模型預(yù)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(shù)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(dǎo)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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