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基于BP神經(jīng)網(wǎng)絡(luò)的農(nóng)機(jī)總動力組合預(yù)測方法
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Combined Prediction Method of Total Power of Agricultural Machinery Based on BP Neural Network
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    摘要:

    鑒于單一預(yù)測模型和線性組合預(yù)測模型的局限性,在確定黑龍江省農(nóng)機(jī)總動力單一預(yù)測模型的基礎(chǔ)上,建立了基于BP神經(jīng)網(wǎng)絡(luò)的非線性農(nóng)機(jī)總動力組合預(yù)測模型。誤差分析表明,該非線性組合預(yù)測模型的擬合平均絕對百分誤差為3.03%,低于一元線性回歸模型、指數(shù)函數(shù)模型、灰色GM(1,1)模型和三次指數(shù)平滑模型的6.26%、4.65%、4.88%和3.72%;稍高于以誤差平方和最小為原則構(gòu)建的線性組合預(yù)測模型的2.86%。用2006~2008年黑龍江省農(nóng)機(jī)總動力進(jìn)行檢驗(yàn)預(yù)測,結(jié)果表明該模型可以有效地提高農(nóng)機(jī)總動力的預(yù)測精度,用該模型預(yù)測了黑龍江省2009~2015年農(nóng)機(jī)總動力。預(yù)測結(jié)果表明,在未來幾年黑龍江省農(nóng)機(jī)總動力將保持快速增長趨勢,到

    Abstract:

    2015年將達(dá)到40537MW。In view of the limitations in single prediction models and linear combined prediction model,nonlinear combined prediction model for total power of agricultural machinery was put forward on the basis of establishing single prediction models for total power of agricultural machinery in Heilongjiang province. The results of error analysis showed that mean absolute percent error of proposed nonlinear combined prediction model was 3.03%, which was lower than 6.26%,4.65%,4.88% and 3.72% of one-variable liner regression model, exponential model, GM(1,1)model and cubic exponent smooth model, and a little higher than 2.86% of the linear combined prediction model based on the minimum sum of error square. Predicting total power of agricultural machinery from 2006 to 2008 proved that this prediction model could efficiently improve prediction accuracy for total power of agricultural machinery. The total powers of agricultural machinery were predicted from 2009 to 2015 in Heilongjiang province. The prediction results showed that total power of agricultural machinery would maintain swift growth tendency in the future several years, it would be 40537MW in 2015.

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鞠金艷,王金武,王金峰.基于BP神經(jīng)網(wǎng)絡(luò)的農(nóng)機(jī)總動力組合預(yù)測方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2010,41(6):87-92.Combined Prediction Method of Total Power of Agricultural Machinery Based on BP Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery,2010,41(6):87-92.

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