A fast identification algorithm with outliers under Box-Cox transformation-based annealing robust radial basis function networks

被引：1

作者：

Chen P.-Y. ^{[1
]}

Wu C.-J. ^{[2
]}

Ko C.-N. ^{[3
]}

Jeng J.-T. ^{[4
]}

机构：

[1] Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliou

[2] Department of Electrical Engineering, National Yunlin University of Science and Technology, Douliou

[3] Department of Automation Engineering, Nan Kai University of Technology, Tsaotun

[4] Department of Computer Science and Information Engineering, National Formosa University, Huwei

来源：

Artificial Life and Robotics | 2009年 / 14卷 / 1期

关键词：

Annealing robust radial basis function networks; Box-Cox transformation; Identification algorithm; Outliers;

D O I：

10.1007/s10015-009-0629-6

中图分类号：

学科分类号：

摘要：

In this article, a Box-Cox transformation-based annealing robust radial basis function networks (ARRBFNs) is proposed for an identification algorithm with outliers. Firstly, a fixed Box-Cox transformation-based ARRBFN model with support vector regression (SVR) is derived to determine the initial structure. Secondly, the results of the SVR are used as the initial structure in the fixed Box-Cox transformation-based ARRBFNs for the identification algorithm with outliers. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the fixed Box-Cox transformation-based ARRBFNs, and applied to adjust the parameters and weights. Hence, the fixed Box-Cox transformation-based ARRBFNs with an ARLA have a fast convergence speed for an identification algorithm with outliers. Finally, the proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with Box-Cox transformation-based radial basis function networks. © International Symposium on Artificial Life and Robotics (ISAROB). 2009.

引用

页码：62 / 66

页数：4

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