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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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 73; H. 10; S. 2196 - 2202
Hauptverfasser: Du, Dajun, Li, Kang, Fei, Minrui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2010
Schlagworte:
Center selection
Computational complexity analysis
Linear-in-the-parameters model
Multi-output fast recursive algorithm (MFRA)
Multi-output radial basis function (RBF) neural network
Computational complexity analysis
Multi-output fast recursive algorithm (MFRA)
Center selection
Linear-in-the-parameters model
Multi-output radial basis function (RBF) neural network
ISSN:0925-2312, 1872-8286
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Beschreibung
Zusammenfassung: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.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2010.01.014