A fast identification algorithm with outliers under Box-Cox transformation-based annealing robust radial basis function networks |
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Authors: | Pi-Yun Chen Chia-Ju Wu Chia-Nan Ko Jin-Tsong Jeng |
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Affiliation: | 1. Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliou, Yunlin, 640, Taiwan 2. Department of Electrical Engineering, National Yunlin University of Science and Technology, Douliou, Yunlin, 640, Taiwan 3. Department of Automation Engineering, Nan Kai University of Technology, Tsaotun, Nantou, 542, Taiwan 4. Department of Computer Science and Information Engineering, National Formosa University, Huwei, Yunlin, 632, Taiwan
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Abstract: | 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. |
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