A fast multi-output RBF neural network construction method

D.J. Du, Kang Li, M.R. Fei

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)

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. (C) 2010 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)2196-2202
Number of pages7
JournalNeurocomputing
Volume73
Issue number10-12
DOIs
Publication statusPublished - Jun 2010

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

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