Multi-index Prediction Model of Wheat Quality Based on Long Short-Term Memory and Generative Adversarial Network
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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。
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) 16.00 30.50 23.18 4.24 降落数值(s) 365.00 630.00 482.81 69.36 沉降值(ml) 19.50 62.00 40.11 13.94 发芽率(%) 0 97.00 71.29 28.96 过氧化物酶(U/g) 1400.00 4100.00 3171.35 667.93 电导率(μs/(cm·g)) 25.50 60.50 39.11 8.75 
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表 2 模型不同训练窗口长度误差对比
窗口长度 2 4 6 8 脂肪酸值 0.260 0.258 0.308 0.328 降落数值 0.325 0.263 0.228 0.277 沉降值 0.356 0.447 0.336 0.407 发芽率 0.652 0.530 0.483 0.511 过氧化物酶 0.424 0.455 0.402 0.415 电导率 0.412 0.324 0.329 0.374
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表 3 LSTM-GAN模型不同结构参数训练误差
隐含层层数 2 3 5 神经元个数 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 降落数值 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 发芽率 0.505 0.560 0.488 0.494 0.610 0.570 0.532 0.582 0.635 0.623 0.657 0.625 过氧化物酶 0.365 0.345 0.340 0.342 0.370 0.280 0.300 0.369 0.325 0.380 0.415 0.409 电导率 0.330 0.370 0.340 0.404 0.440 0.375 0.425 0.417 0.555 0.370 0.435 0.454 综合误差 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 不同筋力小麦多指标预测误差对比
强筋 中筋 弱筋 脂肪酸值 0.275 0.295 0.315 降落数值 0.305 0.290 0.255 沉降值 0.360 0.320 0.245 发芽率 0.422 0.419 0.428 过氧化物酶 0.390 0.350 0.365 电导率 0.290 0.300 0.335
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表 5 不同模型预测误差对比
LSTM-GAN LSTM 线性回归 SVR ANN GM 脂肪酸值 0.275 0.285 0.290 0.303 0.326 0.386 降落数值 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 发芽率 0.488 0.553 0.611 0.467 0.466 0.559 过氧化物酶 0.340 0.378 0.604 0.469 0.460 0.452 电导率 0.340 0.364 0.331 0.372 0.373 0.413 综合误差 2.158 2.391 2.976 2.381 2.484 2.817
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