發(fā)布時間：2018-06-26 12:44

  本文選題：主成分分析 + 凋萎系數(shù)��； 參考：《節(jié)水灌溉》2016年10期

【摘要】：基于黃土高原區(qū)農田耕作層土壤凋萎含水率的測試資料,建立了主成分分析與BP神經網絡相結合土壤凋萎系數(shù)預測模型。通過主成分分析法減少了輸入層神經元個數(shù),優(yōu)化了網絡結構,提高了工作效率。預測值和實測值的相對誤差平均值控制在5%以內,在可接受的范圍,表明利用土壤基本理化參數(shù)預報農田耕作土壤的凋萎含水率是可行的。研究結果在提高傳統(tǒng)神經網絡的預測精度和收斂速度的同時,可為黃土高原區(qū)耕作農田作物用水管理以及促進土壤生產潛力的發(fā)揮提供強有力的理論支撐。
[Abstract]:Based on the test data of soil wilting moisture content in cropland of Loess Plateau, a prediction model of soil wilting coefficient combining principal component analysis (PCA) and BP neural network was established. The number of neurons in input layer is reduced by principal component analysis, the network structure is optimized and the working efficiency is improved. The average relative error between the predicted value and the measured value is controlled within 5%, which indicates that it is feasible to forecast the wilting moisture content of cultivated soil by using the basic physical and chemical parameters of soil. The results of the study not only improve the prediction accuracy and convergence rate of the traditional neural networks, but also provide a strong theoretical support for the management of crop water for farming in the Loess Plateau and for promoting the development of the potential of soil production.
【作者單位】： 太原理工大學水利科學與工程學院;
【基金】：國家自然科學基金項目(40671081)
【分類號】：S152.7

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本文編號：2070426