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| 静电喷头雾化特性预测模型 | |
| 引用本文： | 刘春景,郑加强,王科元.静电喷头雾化特性预测模型[J].农业机械学报,2009,40(4). |
| 作者姓名： | 刘春景 郑加强 王科元 |
| 作者单位： | 1. 南京林业大学机械电子工程学院,南京,210037;蚌埠学院机电系,蚌埠,233030 2. 南京林业大学机械电子工程学院,南京,210037 3. 石河子大学机电工程学院,石河子,832000 |
| 基金项目： | 国家科技支撑计划，安徽省教育厅青年教师资助项目 |
| 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LSSVM方法高近1个数量级,模型预测误差约为标准LSSVM方法的50%,预测精度比常规BP模型高1个数量级. |
| 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 |
Prediction Model for Atomization Performance of Electrostatic Spraying Nozzle |
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| Liu Chunjing,Zheng Jiaqiang,Wang Keyuan.Prediction Model for Atomization Performance of Electrostatic Spraying Nozzle[J].Transactions of the Chinese Society of Agricultural Machinery,2009,40(4). | |
| Authors: | Liu Chunjing Zheng Jiaqiang Wang Keyuan |
| Institution: | 1.College of Mechanical & Electronic Engineering;Nanjing Forestry University;Nanjing 210037;China 2.Department of Mechanical & Electronic Engineering;College of Bengbu;Bengbu 233030;China 3.College of Mechanical & Electronic Engineering;Shihezi University;Shihezi 832000;China |
| Abstract: | 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 ... |
| Keywords: | Electrostatic spraying nozzle Atomization performance Prediction model Modified PSO least square support vector machine |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |