| 基于改进支持向量机的作物叶水势软测量建模 |
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| 引用本文： | 顾幸生,潘晔,卢胜利.基于改进支持向量机的作物叶水势软测量建模[J].同济大学学报(自然科学版),2010,38(11):1669-1674. |
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| 作者姓名： | 顾幸生 潘晔 卢胜利 |
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| 作者单位： | 1. 华东理工大学,自动化研究所,上海,200237
2. 天津工程师范学院,自动化工程系,天津,300222 |
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| 基金项目： | 上海市基础研究重点资助项目，国家自然科学基金，国家"八六三"高技术研究发展计划资助项目 |
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| 摘 要： | 在标准最小二乘支持向量机(least square supportvector machine,LS-SVM)的基础上,利用改进的粒子群算法(i mproved particle swarmopti mization,IPSO)来优化LS-SVM模型参数,提出了基于IPSO-LS-SVM的软测量建模方法,建立了作物叶水势软测量模型.仿真结果表明,该方法比基本LS-SVM和PSO-LS-SVM模型具有更高的精度,能够很好地预测作物叶水势信息.
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| 关 键 词： | 作物叶水势 软测量 最小二乘支持向量机 粒子群 |
Soft Sensor Modeling of Leaf Water Potential Based on Improved Support Vector Machine |
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| GU Xingsheng,PAN Ye and LU Shengli.Soft Sensor Modeling of Leaf Water Potential Based on Improved Support Vector Machine[J].Journal of Tongji University(Natural Science),2010,38(11):1669-1674. |
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| Authors: | GU Xingsheng PAN Ye and LU Shengli |
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| Institution: | Research Institute of Automation,East China University of Science and Technology,Shanghai 200237,China;Research Institute of Automation,East China University of Science and Technology,Shanghai 200237,China;Department of Automation Engineering,Tianjin University of Technology and Education,Tianjin 300222,China |
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| Abstract: | Based on study on least square support vector machine(LS SVM),the paper presents an improved particle swarm optimization (IPSO) algorithm to select the parameters of LS SVM.The soft sensor modeling of the leaf water potential is established based on IPSO LS SVM.Simulation results indicate that the method based on IPSO LS SVM is of a higher accuracy than the basic LS SVM and LS SVM based on PSO,which can well predict leaf water potential. |
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| Keywords: | leaf water potential soft sensing least square support vector machine(LS SVM) particle swarm optimization(PSO) |
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