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基于DAE-BP神經(jīng)網(wǎng)絡(luò )的工業(yè)質(zhì)量預測
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沈陽(yáng)化工大學(xué)

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TP183

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國家自然科學(xué)(61673279);遼寧省教育廳項目(LJ2020021)資助


Prediction of Industrial Quality Based on DAE-BP Neural Network
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    摘要:

    BP神經(jīng)網(wǎng)絡(luò )因具有良好的非線(xiàn)性擬合能力,在建立預測模型中得到廣泛應用。但化工過(guò)程數據不僅存在非線(xiàn)性特征,而且難以避免受噪聲影響,造成數據波動(dòng)從而影響預測模型準確性。為此,提出一種降噪自編碼器融合反向傳播算法(簡(jiǎn)稱(chēng)為,DAE-BP)的化工過(guò)程質(zhì)量預測方法。首先,采用無(wú)監督學(xué)習模型降噪自編碼器完成初始數據的噪聲消除,其具有噪聲魯棒性的特點(diǎn),在數據受到損壞的情況下可盡可能地恢復數據的原始狀態(tài),有利于進(jìn)一步的質(zhì)量預測。在此基礎上,將獲取的數據特征作為有監督學(xué)習模型BP神經(jīng)網(wǎng)絡(luò )的輸入以獲得可靠的預測結果。在脫丁烷塔化工過(guò)程實(shí)例上驗證方法有效性。并與單一BP算法、主成分分析(PCA)及自編碼器(AE)改進(jìn)的BP算法作為對照。結果表明,經(jīng)過(guò)DAE改進(jìn)后的BP算法預測誤差為1.2%,相比單一的BP算法提高了3.2%精度,較PCA-BP及AE-BP預測誤差精度分別提高了2.3%、1.9%,表現出最好的預測性能。

    Abstract:

    BP neural network has been widely used in building prediction models because of its good nonlinear fitting ability. However, the chemical process data not only has nonlinear characteristics, but also is difficult to avoid the influence of noise, which causes data fluctuation and affects the accuracy of prediction model. Therefore, a DAE-BP method for chemical process quality prediction based on denoising autoencoder fusion was proposed. Firstly, the unsupervised learning model denoising autoencoder is used to eliminate the noise of the initial data, which has the characteristics of noise robustness and can restore the original state of the data as far as possible in the case of data damage, which is conducive to further quality prediction. On this basis, the obtained data features are used as the input of BP neural network of supervised learning model to obtain reliable prediction results. The effectiveness of the method was verified by an example of chemical process of debutanizer column. The results were compared with the single BP algorithm, principal component analysis (PCA) and autoencoder (AE) improved BP algorithm. The results show that the prediction error of BP algorithm improved by DAE is 1.2%, which is 3.2% higher than that of the single BP algorithm, 2.3% higher than that of PCA-BP and 1.9% higher than that of AE-BP, showing the best prediction performance.

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郭小萍,馬美卉,李元.基于DAE-BP神經(jīng)網(wǎng)絡(luò )的工業(yè)質(zhì)量預測計算機測量與控制[J].,2023,31(1):181-186.

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歷史
  • 收稿日期:2022-06-06
  • 最后修改日期:2022-06-28
  • 錄用日期:2022-06-29
  • 在線(xiàn)發(fā)布日期: 2023-01-16
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