Linear regression model-guided clustering for training RBF networks for regression problems

  • Authors:
  • Antonino Staiano;Roberto Tagliaferri;Witold Pedrycz

  • Affiliations:
  • DMI, Università di Salerno, Baronissi (Sa), Italy;DMI, Università di Salerno, Baronissi (Sa), Italy;ECERF – University of Alberta, Edmonton, Canada

  • Venue:
  • WILF'03 Proceedings of the 5th international conference on Fuzzy Logic and Applications
  • Year:
  • 2003

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Abstract

In this paper, we describe a novel approach to fuzzy clustering which organizes the data in clusters on the basis of the input data and builds a 'prototype' regression function as a summation of linear local regression models to guide the clustering process. This methodology is shown to be effective in the training of RBFNN's. It is shown that the performance of such networks is better than other types of networks.