| 基于改进k-最近邻回归算法的软测量建模 |
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| 引用本文： | 叶涛,朱学峰,李向阳,史步海. 基于改进k-最近邻回归算法的软测量建模[J]. 自动化学报, 2007, 33(9): 996-999. DOI: 10.1360/aas-007-0996 |
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| 作者姓名： | 叶涛 朱学峰 李向阳 史步海 |
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| 作者单位： | 华南理工大学自动化科学与工程学院,广州,510640 |
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| 基金项目： | 国家自然科学基金;广东省自然科学基金 |
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| 摘 要： | 机器学习回归方法被广泛应用于复杂工业过程的软测量建模k-最近邻(kNN)算法是一种流行的学习算法,可用于函数回归问题.然而,传统kNN算法存在运行效率低、距离计算忽略特征权值的缺点.本文引入了二次型距离定义和样本集剪辑算法,改进了传统kNN回归算法,并将改进的算法用于工业过程软测量建模.仿真实验得到了一些有益的结论.
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| 关 键 词： | k-最近邻算法 二次型距离 软测量 纸浆Kappa值 |
| 收稿时间： | 2006-03-08 |
| 修稿时间： | 2006-03-08 |
Soft Sensor Modeling Based on a Modified k-Nearest Neighbor Regression Algorithm |
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| YE Tao,ZHU Xue-Feng,LI Xiang-Yang,SHI Bu-Hai. Soft Sensor Modeling Based on a Modified k-Nearest Neighbor Regression Algorithm[J]. Acta Automatica Sinica, 2007, 33(9): 996-999. DOI: 10.1360/aas-007-0996 |
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| Authors: | YE Tao ZHU Xue-Feng LI Xiang-Yang SHI Bu-Hai |
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| Affiliation: | College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640 |
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| Abstract: | Recently,machine learning regression algorithms are widely applied to soft sensor modeling for complex industrial processes.The k-nearest neighbor(kNN)algorithm is a popular learning algorithm for solving regression problems.However,the traditional kNN algorithm has low efficiency and ignores the fea- ture weights in distance computing.Using a quadratic distance definition and a data set editing algorithm,we have modified the traditional kNN regression algorithm.The modified algorithm is applied to soft sensor modeling and some useful conclusions are reached. |
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| Keywords: | k-nearest neighbor algorithm quadratic distance soft sensing pulp Kappa number |
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