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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.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 feature 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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Key words:
- k-nearest neighbor algorithm /
- quadratic distance /
- soft sensing /
- pulp Kappa number
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