果树施药仿形喷雾神经网络模型及其应用

在试验的基础上，建立了反映果树施药仿形喷雾过程参量与分布质量系数之间映射关系的BP神经网络模型，在建模中运用了正交试验设计、交叉评价网络训练法、样本标准化处理和主元分析等技术，对网络结构及其参数进行了优选。结果显示，网络模型输出同试验结果相关系数R达到0.99，表明具有广泛的适应性。同时，该网络可以实现各种定量分析计算，例如：预测在特定过程参量下的分布质量系数，或者根据指定的效果目标，确定合适的喷雾参量等。

Neural network model for profile modeling spray of chemical to fruit trees and its applications

Based on the experiment, a BP neural network model was developed to map the relationship of process variables and distributing quality coefficient of Profile Modeling Spray of the Fruit Trees. The structure and parameters of BP neural network were optimized by using the methods of orthogonal experiments, intercross evaluating and training, normalization of the network training set and principal component analysis. The result shows that the correlation coefficient R between simulating outputs of the BP network and the results of experiments is 0.99, which has wide adaptability. And the BP network can be used conveniently to carry out various quantificational calculations. For example, the distributing quality coefficient under specific process variables can be estimated, or appropriate spray variables can be determined according to given production target.