Study on the Model of Excessive Staminate Catkin Thinning of Proterandrous Walnut Based on Quadratic Polynomial Regression Equation and BP Artificial Neural Network  

雄先型核桃雄花疏除的二次回归与BP神经网络模型研究（英文）

作　　者：王贤萍[1] 曹贵寿[1] 杨晓华[1] 张倩茹[1] 李凯[1] 李鸿雁[1] 段泽敏[1] 

机构地区：[1]山西省农业科学院果树研究所/果树种质创制与利用山西省重点实验室

出　　处：《Agricultural Science & Technology》2015年第6期1295-1300,共6页农业科学与技术（英文版）

基　　金：Supported by Key Science and Technology Program of Shanxi Province,China(002023)~~

摘　　要：The excessive staminate catkin thinning （emasculation） of proterandrous walnut is an important management measure for improving yield. To improve the excessive staminate catkin thinning efficiency, the model of quadratic polynomial regression equation and BP artificial neural network was developed. The effects of ethephon, gibberel in and mepiquat on shedding rate of staminate catkin of pro-terandrous walnut were investigated by modeling field test. Based on the modeling test results, the excessive staminate catkin thinning model of quadratic polynomial regression equation and BP artificial neural network was established, and it was validated by field test next year. The test data were divided into training set, vali-dation set and test set. The total 20 sets of data obtained from the modeling field test were randomly divided into training set （17） and validation set （3） by central composite design （quadric rotational regression test design）, and the data obtained from the next-year field test were divided into the test set. The topological struc-ture of BP artificial neural network was 3-5-1. The results showed that the pre-diction errors of BP neural network for samples from the validation set were 1.355 0%, 0.429 1% and 0.353 8%, respectively; the difference between the predicted value by the BP neural network and validated value by field test was 2.04%, and the difference between the predicted value by the regression equation and validated value by field test was 3.12%; the prediction accuracy of BP neural network was over 1.0% higher than that of regression equation. The effective combination of quadratic polynomial stepwise regression and BP artificial neural network wil not only help to determine the effect of independent parameter but also improve the prediction accuracy.雄先型核桃雄花疏除(去雄)是提高产量的重要管理措施,为提高核桃去雄的效率,建立二次回归与BP神经网络模型。分别以乙烯利、赤霉素和甲哌鎓为自变量和核桃雄花脱落率为响应指标,进行田间建模试验,建立了二次多项式回归方程和BP神经网络模型,并于翌年进行BP模型田间确认试验。试验数据分为训练集、确认集和试验集,中心组合(二次旋转回归试验设计)田间建模试验得到的20组数据随机划为训练集(17)和确认集(3)数据,试验集为翌年田间确认试验得到的数据,BP神经网络的拓扑结构为3-5-1。1BP神经网络对确认集样本的预测值误差分别为1.3550%、0.4291%、0.3538%;2BP神经网络的预测值与田间确认试验结果相差为2.04%,回归预测值与田间确认试验结果相差为3.12%;3BP神经网络预测比回归预测提高预测精度1.0%以上。将二次多项式逐步回归分析和BP神经网络方法有效的结合使用,既可明确各因子的作用效应亦可得到相对准确的预测结果。

关 键 词：WALNUT THINNING BP artificial neural network Regression PREDICTION 

分 类 号：S664.1[农业科学—果树学]