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基于BP神經(jīng)網(wǎng)絡(luò)的果蔬熱導(dǎo)率預(yù)測(cè)模型
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Prediction Model of Thermal Conductivities of Fruits and Vegetables Based on BP Neural Networks
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    摘要:

    通過(guò)微熱探針?lè)y(cè)試裝置研究了30個(gè)品種的果蔬熱導(dǎo)率與可溶性固形物含量,、含水率、密度和硬度等因素的變化關(guān)系,，提出了一種基于BP神經(jīng)網(wǎng)絡(luò)的果蔬熱導(dǎo)率預(yù)測(cè)模型,，并根據(jù)誤差比較分析進(jìn)行了模型優(yōu)化。結(jié)果表明,，該優(yōu)化網(wǎng)絡(luò)模型具有較好的熱導(dǎo)率預(yù)測(cè)效果,，平均相對(duì)誤差為1.11%,，平均絕對(duì)誤差為0.0057W/(m?K),，可以用于果蔬貯藏加工業(yè)中果蔬熱傳遞過(guò)程的計(jì)算,。

    Abstract:

    The relationships between thermal conductivities and soluble solid contents, water contents, temperature, density and hardness of 30 types fruits and vegetables were studied by the tepid probe test system. A BP artificial neural network model was presented and optimized for the prediction of thermal conductivities of fruits and vegetables according to the error analysis. The result showed that the optimal model was able to predict thermal conductivity with a mean relative error of 1.11%, a mean absolute error of 0.0057W/(m?K). The model can be incorporated in heat transfer calculations during fruits and vegetables processing. 

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張敏,鐘志友,楊樂(lè),趙惠忠,陳健華,車貞花.基于BP神經(jīng)網(wǎng)絡(luò)的果蔬熱導(dǎo)率預(yù)測(cè)模型[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2010,41(10):117-121.Prediction Model of Thermal Conductivities of Fruits and Vegetables Based on BP Neural Networks[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(10):117-121.

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