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.
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Awad, M., Pomares, H., Herrera, L.J., González, J., Guillén, A., Rojas, F. (2005). Approximating I/O Data Using Radial Basis Functions: A New Clustering-Based Approach. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_36
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DOI: https://doi.org/10.1007/11494669_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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