| 局部惩罚加权核偏最小二乘算法及其应用 |
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| 引用本文： | 杨慧中,陈定三. 局部惩罚加权核偏最小二乘算法及其应用[J]. 控制工程, 2011, 18(6): 886-889 |
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| 作者姓名： | 杨慧中 陈定三 |
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| 作者单位： | 江南大学轻工过程先进控制教育部重点实验室,江苏无锡,214122 |
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| 基金项目： | 国家自然科学基金资助项目(60674092); 江苏省高技术研究项目(BG20060010); 江南大学创新团队发展计划资助项目 |
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| 摘 要： | 为改善软测量模型精度,提出了一种局部惩罚加权核偏最小二乘算法.该方法通过核映射将原始输入映射到高维特征空间实现对非线性问题的线性化处理,并通过偏最小二乘算法进行主成分提取,降低数据维数；对由主成分构成的新数据集,依据局部学习思想构建局部惩罚加权最小二采回归模型,降低模型对异常数据的敏感度、优化模型参数.鉴于多模型可以改...
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| 关 键 词： | 核偏最小二乘 局部学习 惩罚加权最小二乘 软测量 多模型 |
Local Penalized Weighted Kernel Partial Least Squares Algorithm and Its Applications |
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| YANG Hui-zhong , CHEN Ding-san. Local Penalized Weighted Kernel Partial Least Squares Algorithm and Its Applications[J]. Control Engineering of China, 2011, 18(6): 886-889 |
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| Authors: | YANG Hui-zhong CHEN Ding-san |
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| Affiliation: | YANG Hui-zhong,CHEN Ding-san(Key Laboratory of the Ministry of Education for Advanced Control in Light Industry Process,Jiangnan University,Wuxi 214122,China) |
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| Abstract: | In order to improve the accuracy of soft-sensor model,a novel penalized weighted kernel partial least squares algorithm is presented.The original inputs are mapped into a high dimensional feature space to realize the linearization of nonlinear problems.The partial least squares algorithm is used to extract the principal component to reduce the dimensional of data.According to the local learning theory,a local penalized weighted least squares regression model is constructed based on the new data set formed b... |
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| Keywords: | kernel partial least squares local learning penalized weighted least squares soft sensor multiple models |
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