A fast multi-output RBF neural network construction method
This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance mat...
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| Published in: | Neurocomputing (Amsterdam) Vol. 73; no. 10; pp. 2196 - 2202 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.06.2010
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| ISSN: | 0925-2312, 1872-8286 |
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| Abstract | This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. |
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| AbstractList | This paper investigates the center selection of multi-output radial basis function (RBF) networks, and a multi-output fast recursive algorithm (MFRA) is proposed. This method can not only reveal the significance of each candidate center based on the reduction in the trace of the error covariance matrix, but also can estimate the network weights simultaneously using a back substitution approach. The main contribution is that the center selection procedure and the weight estimation are performed within a well-defined regression context, leading to a significantly reduced computational complexity. The efficiency of the algorithm is confirmed by a computational complexity analysis, and simulation results demonstrate its effectiveness. |
| Author | Du, Dajun Fei, Minrui Li, Kang |
| Author_xml | – sequence: 1 givenname: Dajun surname: Du fullname: Du, Dajun email: ddj@shu.edu.cn, ddj559@hotmail.com organization: Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200072, China – sequence: 2 givenname: Kang surname: Li fullname: Li, Kang organization: School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast BT9 5 AH, UK – sequence: 3 givenname: Minrui surname: Fei fullname: Fei, Minrui organization: Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronical Engineering and Automation, Shanghai University, Shanghai 200072, China |
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| Keywords | Computational complexity analysis Multi-output fast recursive algorithm (MFRA) Center selection Linear-in-the-parameters model Multi-output radial basis function (RBF) neural network |
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| SubjectTerms | Center selection Computational complexity analysis Linear-in-the-parameters model Multi-output fast recursive algorithm (MFRA) Multi-output radial basis function (RBF) neural network |
| Title | A fast multi-output RBF neural network construction method |
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