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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Vydáno v:	Neurocomputing (Amsterdam) Ročník 73; číslo 10; s. 2196 - 2202
Hlavní autoři:	Du, Dajun, Li, Kang, Fei, Minrui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.06.2010
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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