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基于RSM和BP—AdaBoost—GA的紅茶發(fā)酵性能參數(shù)優(yōu)化
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國(guó)家自然科學(xué)基金項(xiàng)目(31271875)、浙江省自然科學(xué)基金項(xiàng)目(Y16C160009)和浙江省重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2015C02001)


Parameter Optimization of Black Tea Fermentation Machine Based on RSM and BP—AdaBoost—GA
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    摘要:

    為明確自行設(shè)計(jì)的滾筒式紅茶發(fā)酵機(jī)性能參數(shù),以無(wú)量綱化的綜合評(píng)分為發(fā)酵品質(zhì)評(píng)價(jià)指標(biāo),采用響應(yīng)面法和基于改進(jìn)型神經(jīng)網(wǎng)絡(luò)的遺傳算法(BP—AdaBoost—GA)對(duì)影響發(fā)酵品質(zhì)的3個(gè)因素(發(fā)酵溫度、發(fā)酵時(shí)間、翻拌間隔)進(jìn)行優(yōu)化,并對(duì)2種方法的優(yōu)化效果進(jìn)行比較。結(jié)果表明,各因素對(duì)發(fā)酵品質(zhì)的影響重要性順序?yàn)椋喊l(fā)酵溫度、翻拌間隔、發(fā)酵時(shí)間;采用響應(yīng)面法優(yōu)化,當(dāng)發(fā)酵溫度、發(fā)酵時(shí)間、翻拌間隔分別為25℃、150min、20min時(shí),綜合評(píng)分預(yù)測(cè)值和實(shí)際值分別為0.863和0.856,相對(duì)誤差為0.8%;而采用BP—AdaBoost—GA優(yōu)化,當(dāng)發(fā)酵溫度、發(fā)酵時(shí)間、翻拌間隔分別為27℃、170min、25min時(shí),預(yù)測(cè)值和實(shí)際值分別為0.871和0.868,相對(duì)誤差為0.3%;BP—AdaBoost預(yù)測(cè)模型的決定系數(shù)和相對(duì)分析誤差分別為0.994和18.456,高于響應(yīng)面法的0.988和9.577,且預(yù)測(cè)均方根誤差較低,為0.017。在紅茶發(fā)酵工藝的參數(shù)優(yōu)化中,采用BP—AdaBoost—GA方法能比響應(yīng)面法更好地?cái)M合模型,以及在全局變量范圍內(nèi)推導(dǎo)最優(yōu)發(fā)酵條件。

    Abstract:

    Fermentation is the key procedure in processing of congou black tea, which directly decides the quality and flavor of tea products. Fermentation experiments were conducted on a novel drum-type fermentation machine as the platform, the performance parameters of fermentation machine were clarified. Methodologically, with dimensionless comprehensive scores as a measure of fermentation quality, response surface methodology (RSM) and back-propagation adaptive boosting based genetic algorithm (BP—AdaBoost—GA) were used separately to optimize three parameters (fermentation temperature x1, fermentation time x2, rotational interval x3) that affect fermentation quality. Also the optimizing effects of RSM and BP—AdaBoost—GA were compared. Results showed that the importance degrees of the three parameters ranked as x1>x3>x2. With RSM at x1=25℃, x2=150min and x3=20min, the predicted and actual values of comprehensive scores were 0.863 and 0.856, respectively, showing relative error of 0.8%. With BP—AdaBoost—GA at x1=27℃, x2=170min and x3=25min, the predicted and actual values of comprehensive scores were 0.871 and 0.868, respectively, showing relative error of 0.3%. When the BP—AdaBoost had seven nodes in the hidden layer and a prediction error threshold of 0.25, its determination coefficient was greater than that of RSM (0.994 vs 0.988), and it had lower root mean square error of prediction (RMSEP) of 0.017 and residual predictive deviation (RPD) equaled to 18.456. Both RSM and BP—AdaBoost—GA were feasible for optimization of fermentation parameters. However, the fitting ability of RSM was limited because it was based on quadratic polynomial regression, while the fitting ability over experimental data was limited. The algorithm combining improved neural network and GA had higher global extremum prediction ability and higher accuracy. Thus, it can be concluded that even though RSM was the most widely used method for fermentation parameter optimization, BP—AdaBoost—GA methodology may present a better alternative. In the meantime, the rotation function had both advantages and disadvantages on the fermentation quality of black tea, moderate rotation and mixing material can enhance the quality of black tea and shorten the fermentation time.

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董春旺,趙杰文,朱宏凱,袁海波,葉陽(yáng),陳全勝.基于RSM和BP—AdaBoost—GA的紅茶發(fā)酵性能參數(shù)優(yōu)化[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2017,48(5):335-342. DONG Chunwang, ZHAO Jiewen, ZHU Hongkai, YUAN Haibo, YE Yang, CHEN Quansheng. Parameter Optimization of Black Tea Fermentation Machine Based on RSM and BP—AdaBoost—GA[J]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(5):335-342.

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  • 收稿日期:2016-11-02
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  • 在線發(fā)布日期: 2017-05-10
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