排灌机械工程学报
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排灌机械工程学报  2014, Vol. 32 Issue (9): 788-794    DOI: 10.3969/j.issn.1674-8530.13.0245
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基于RSM和GA的温室风送式弥雾机喷雾效果优化分析
钟丽平1,2, 祁力钧1,3, 冀荣华4, 袁雪5, 梅银成1, 高春花1
1.中国农业大学工学院, 北京 100083; 2.桂林理工大学机械与控制工程学院, 广西 桂林 541004; 3.现代农业装备优化设计北京市重点实验室, 北京 100083; 4.中国农业大学信息与电气工程学院, 北京 100083; 5.中国农业科学院, 北京 100083
Analysis of optimum drop size distribution of greenhouse air-assisted mist sprayer by using response surface method and genetic algorithm
Zhong Liping1,2, Qi Lijun1,3, Ji Ronghua4, Yuan Xue5, Mei Yincheng1, Gao Chunhua1
1.College of Engineering, China Agricultural University, Beijing 100083, China; 2.College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China; 3. Beijing Key Laboratory of Optimization Design of Modern Agricultural Equipment, Beijing 100083, China; 4.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 5.Chinese Academy of Agricultural Sciences, Beijing 100083, China
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摘要 为了研究不同喷雾参数优化方法对温室风送式弥雾机喷雾效果的影响,在试验的基础上,分别运用二次多项式回归和BP神经网络,建立了温室风送式弥雾机喷雾分布均匀性响应面模型.结果表明: BP神经网络响应面模型的相关系数、均方根误差分别为0.987 1,0.134 0,而二次多项式响应面模型的相关系数、均方根误差分别为0.928 2,0.215 9,BP神经网络模型较高的相关系数和较低的均方根误差说明其拟合精度较高;对二次多项式回归模型寻优,预测的雾滴分布变异系数最小值为1.47%,实际值为1.58%;采用BP神经网络协同遗传算法寻优,雾滴分布变异系数预测值和实际值分别为1.21%,1.28%;表明在喷雾参数优化中,基于BP神经网络的遗传算法比二次多项式响应面法具有更好的准确性. 
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钟丽平
祁力钧
冀荣华
袁雪
梅银成
高春花
关键词喷雾参数   响应面   神经网络   比较   遗传算法     
Abstract: In order to clarify the influences of operational parameters on drop size distribution issued by a greenhouse air-assisted mist sprayer, a series of spraying experiments are conducted, then two response surface models about the drop size distribution coefficient are correlated to the operational parameters by using quadratic polynomial regression and BP neural network method to obtain the optimum operational parameters combination. It is shown that the correlation coefficient and root-mean-square error are 0.987 1 and 0.134 0 respectively for the BP neural network model, compared with 0.928 2 and 0.215 9 for the response surface model established by the quadratic polynomial regression, suggesting the former is the best model. According to the quadratic polynomial response surface model, the minimum drop size distribution coefficient is as high as 1.47%, compared with 1.58% experimental measurement. Based on the BP neural network model, however, the minimum drop size distribution coefficient predicted by genetic algorithm is just 1.21% against 1.28% experimental observation. Ob-viously, the BP neural network method combined with genetic algorithm exhibits a better accuracy than the response surface method in the operational parameters optimization of air-assisted mist sprayer.
Key wordsspraying parameter   response surface methodology   BP neural network   comparison   genetic algorithm   
收稿日期: 2013-12-06;
基金资助:

农业部农业科技专项资助项目(201203025);中国农业大学研究生科研创新专项资助项目(2013YJ007)

通讯作者: 钟丽平(1989—),女,江西瑞金人,助教,硕士(zlp19891230@163.com),主要从事植保机械与施药技术研究.   
作者简介: 祁力钧(1963—),男,甘肃榆中人,教授,博士生导师(通信作者,qilijun@cau.edu.cn),主要从事植保机械与施药技术研究.
引用本文:   
钟丽平,,祁力钧等. 基于RSM和GA的温室风送式弥雾机喷雾效果优化分析[J]. 排灌机械工程学报, 2014, 32(9): 788-794.
ZHONG Li-Ping-,,QI Li-Jun- et al. Analysis of optimum drop size distribution of greenhouse air-assisted mist sprayer by using response surface method and genetic algorithm[J]. Journal of Drainage and Irrigation Machinery Engin, 2014, 32(9): 788-794.
 
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