Weighted LS-SVMR-Based System Identification with Outliers
Weighted LS-SVMR-Based System Identification with Outliers
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DOI：
10.1145/3351917.3351940
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Congjun Ma;Haipeng Wang;T. Zhao;S. Dian
中科院分区：
文献类型：
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作者：
Congjun Ma;Haipeng Wang;T. Zhao;S. Dian
在系统识别中应用了大量的方法，其中基于数据驱动的方法越来越受欢迎。通常我们会忽略待建模系统中不存在异常点，但在现实中这是无法达到的。为了提高对具有异常值的系统的识别精度，需要具有鲁棒性的有利方法。本文分析了加权最小二乘支持向量机回归（LS-SVMR）在随机离群值下的系统识别领域的优越性，并与LS-SVMR进行了比较。
Plenty of methods applied in system identification, while those based on data-driven are increasingly popular. Usually we ignore the absence of outliers among the system to be modeled, but it is unreachable in reality. To improve the precision of identification towards system with outliers, advantageous approaches with robustness are needed. This study analyzes the superiority of weighted Least Square Support Vector Machine Regression (LS-SVMR) in the field of system identification under random outliers, and compare it with LS-SVMR mainly.