Approximating i/o data using radial basis functions: a new clustering-based approach

Authors:
Mohammed Awad;Héctor Pomares;Luis Javier Herrera;Jesús González;Alberto Guillén;Fernando Rojas
Affiliations:
Dept. of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Dept. of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Dept. of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Dept. of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Dept. of Computer Architecture and Computer Technology, University of Granada, Granada, Spain;Dept. of Computer Architecture and Computer Technology, University of Granada, Granada, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

In this paper, we deal with the problem of function approximation from a given set of input/output data. This problem consists of analyzing these training examples so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O data using radial basis functions (RBFs). This approach is based on a new efficient method of clustering of the centres of the RBF Network (RBFN); it uses the objective output of the RBFN to move the clusters instead of just the input values of the I/O data. This method of clustering, especially designed for function approximation problems, improves the performance of the approximator system obtained, compared with other models derived from traditional algorithms.