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 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 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 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 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 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 0.275 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 下載: 導出CSV
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 下載: 導出CSV
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KALSA K K, SUBRAMANYAM B, DEMISSIE G, et al. Evaluation of postharvest preservation strategies for stored wheat seed in Ethiopia[J]. Journal of Stored Products Research, 2019, 81: 53–61. doi: 10.1016/j.jspr.2019.01.001 ZHANG Shuaibing, Lü Yangyong, WANG Yuli, et al. Physiochemical changes in wheat of different hardnesses during storage[J]. Journal of Stored Products Research, 2017, 72: 161–165. doi: 10.1016/j.jspr.2017.05.002 陳紅松, 陳京九. 基于循環(huán)神經網絡的無線網絡入侵檢測分類模型構建與優(yōu)化研究[J]. 電子與信息學報, 2019, 41(6): 1427–1433. doi: 10.11999/JEIT180691CHEN Hongsong and CHEN Jingjiu. Recurrent neural networks based wireless network intrusion detection and classification model construction and optimization[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1427–1433. doi: 10.11999/JEIT180691 XU Peng, DU Rui, ZHANG Zhongbao, et al. Predicting pipeline leakage in petrochemical system through GAN and LSTM[J]. Knowledge-Based Systems, 2019, 175: 50–61. doi: 10.1016/j.knosys.2019.03.013 MAHASSENI B, LAM M, and TODOROVIC S. Unsupervised video summarization with adversarial lstm networks[C]. 2017 IEEE conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2982–2991. doi: 10.1109/CVPR.2017.318. YANG Yang, ZHOU Jie, AI Jiangbo, et al. Video captioning by adversarial LSTM[J]. IEEE Transactions on Image Processing, 2018, 27(11): 5600–5611. doi: 10.1109/TIP.2018.2855422 GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]. The 27th International Conference on Neural Information Processing Systems, Montreal, Canada, 2014: 2672–2680. 曹志義, 牛少彰, 張繼威. 基于半監(jiān)督學習生成對抗網絡的人臉還原算法研究[J]. 電子與信息學報, 2018, 40(2): 323–330. doi: 10.11999/JEIT170357CAO Zhiyi, NIU Shaozhang, and ZHANG Jiwei. Research on face reduction algorithm based on generative adversarial nets with semi-supervised learning[J]. Journal of Electronics &Information Technology, 2018, 40(2): 323–330. doi: 10.11999/JEIT170357 蔣華偉, 張磊, 周同星. 基于信息熵的小麥儲藏品質多指標權重模型研究[J]. 中國糧油學報, 2020, 35(6): 105–113. doi: 10.3969/j.issn.1003-0174.2020.06.016JIANG Huawei, ZHANG Lei, and ZHOU Tongxing. Research on multi-index weight model of wheat storage quality based on information entropy[J]. Journal of the Chinese Cereals and Oils Association, 2020, 35(6): 105–113. doi: 10.3969/j.issn.1003-0174.2020.06.016 劉威, 劉尚, 白潤才, 等. 互學習神經網絡訓練方法研究[J]. 計算機學報, 2017, 40(6): 1291–1308. doi: 10.11897/SP.J.1016.2017.01291LIU Wei, LIU Shang, BAI Runcai, et al. Research of mutual learning neural network training method[J]. Chinese Journal of Computers, 2017, 40(6): 1291–1308. doi: 10.11897/SP.J.1016.2017.01291 高艷娜. 小麥產后品質變化規(guī)律研究[D]. [碩士論文], 河南工業(yè)大學, 2010.GAO Yanna. Study on the changes of postpartum quality in wheat[D]. [Master dissertation], Henan University of Technology, 2010. FRIEDMAN L and KOMOGORTSEV O V. Assessment of the effectiveness of seven biometric feature normalization techniques[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(10): 2528–2536. doi: 10.1109/TIFS.2019.2904844 GREFF K, SRIVASTAVA R K, KOUTNíK J, et al. LSTM: A search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2222–2232. doi: 10.1109/TNNLS.2016.2582924 FANG Tingting and LAHDELMA R. Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system[J]. Applied Energy, 2016, 179: 544–552. doi: 10.1016/j.apenergy.2016.06.133 XU Jie, XU Chen, ZOU Bin, et al. New incremental learning algorithm with support vector machines[J]. IEEE Transactions on Systems, Man, and Cybernetics; Systems, 2019, 49(11): 2230–2241. doi: 10.1109/tsmc.2018.2791511 VILLARRUBIA G, DE PAZ J F, CHAMOSO P, et al. Artificial neural networks used in optimization problems[J]. Neurocomputing, 2018, 272: 10–16. doi: 10.1016/j.neucom.2017.04.075 DING Song, HIPEL K W, and DANG Yaoguo. Forecasting China’s electricity consumption using a new grey prediction model[J]. Energy, 2018, 149: 314–328. doi: 10.1016/j.energy.2018.01.169 -