摘要
将一种基于改进粒子群优化最小二乘支持向量机的预测模型引入静电喷雾雾化性能预测领域,并给出了相应的步骤和算法。该模型能方便地预测喷雾参数对喷头雾化性能的影响,有助于正确认识喷头雾化性能随喷雾参数的变化规律。通过具体实例及与其他几种预测方法的对比表明,在相同样本条件下,其模型构造速度比标准LS-SVM方法高近1个数量级,模型预测误差约为标准LS-SVM方法的50%,预测精度比常规BP模型高1个数量级。
On the basis of analyzing disadvantages of conventional prediction model, a novel prediction model based on modified PSO least square support vector machine was proposed. Based on the new model, the design steps and learning algorithm were given. The practical experimental results show that the construction speed of this modified PSO LS - SVM model is 10 times less than that of the LS - SVM model, while the prediction error is 50%. Moreover, compared with BP model, the prediction accuracy is about 10 times higher than that of the former. The effects of electrostatic spraying parameters on atomization performance of electrostatic spraying nozzle can be predicted with the limited test data. Thus the variation law of atomization performance of electrostatic spraying nozzle following electrostatic spraying parameters can be obtained.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2009年第4期63-68,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家“十一五”科技支撑计划资助项目(2006BAD08A1203)
安徽省教育厅青年教师资助项目(2007jq1126)
关键词
静电喷头
雾化性能
预测模型
改进粒子群优化最小二乘支持向量机
Electrostatic spraying nozzle, Atomization performance, Prediction model, ModifiedPSO least square support vector machine
