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基于BP神經(jīng)網(wǎng)絡(luò)的牡丹花熱風(fēng)干燥含水率預(yù)測模型
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河南省杰出青年基金資助項目（084100510005）;洛陽市科技攻關(guān)項目（0901048）


Moisture Content Prediction Modeling of Hot-air Drying for Pressed Peony Based on BP Neural Network
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    摘要:

    針對熱風(fēng)干燥制作牡丹壓花時含水率不便實時測定的問題,，探討了干燥過程中熱風(fēng)溫度、風(fēng)速、壓花板孔密度和牡丹花初始質(zhì)量對干燥速率的影響。利用BP神經(jīng)網(wǎng)絡(luò)建立了干燥時間、熱風(fēng)溫度,、風(fēng)速、牡丹花初始質(zhì)量、壓花板孔密度與牡丹花干燥過程中含水率之間的關(guān)系模型,，采用Matlab神經(jīng)網(wǎng)絡(luò)工具箱對模型參數(shù)進(jìn)行訓(xùn)練和模擬。結(jié)果表明,，利用神經(jīng)網(wǎng)絡(luò)建立的模型仿真結(jié)果與實測值接近,，預(yù)測性較好。

    Abstract:

    Pressed peony was made by hot-air drying method. The influence of temperature of hot-air, speed of hot-air, drying board's hole density and the initial mass of peony on drying speed was discussed. Relationship model between drying time, temperature of hot-air, speed of hot-air, drying board's hole density, the initial mass and moisture content was built by using BP neural network. Parameters in the proposed model were trained and simulated in Matlab. The results indicated that the simulated values of the drying moisture content were close to the measured values.

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朱文學(xué),孫淑紅,陳鵬濤,陳志宏.基于BP神經(jīng)網(wǎng)絡(luò)的牡丹花熱風(fēng)干燥含水率預(yù)測模型[J].農(nóng)業(yè)機(jī)械學(xué)報,2011,42(8):128-130,137. Zhu Wenxue, Sun Shuhong, Chen Pengtao, Chen Zhihong. Moisture Content Prediction Modeling of Hot-air Drying for Pressed Peony Based on BP Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(8):128-130,137.

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