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...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	Neurocomputing (Amsterdam) Ročník 73; číslo 10; s. 2196 - 2202
Hlavní autori:	Du, Dajun, Li, Kang, Fei, Minrui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.06.2010
Predmet:	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 prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

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