Abstract
A new approach is presented for the approximation of a scalar function defined on a discrete set of points. The method is based on the application of functional networks and the Lagrange interpolation formula. The interpolation mechanism of the separable functional networks when the neuron functions are approximated by Lagrange polynomials, is explored. The coefficients of the Lagrange interpolation formula are estimated during the learning of the functional network by simply solving a linear system of equations. Finally, several examples show the effectiveness of the proposed interpolation method.
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© 2006 Springer-Verlag Berlin Heidelberg
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Solares, C., Vieira, E.W., Mínguez, R. (2006). Functional Networks and the Lagrange Polynomial Interpolation. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_48
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DOI: https://doi.org/10.1007/11875581_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
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