Abstract
Wavelet neural networks (WNN) have recently attracted great interest, because of their advantages over radial basis function networks (RBFN) as they are universal approximators. In this paper we present a novel wavelet neural network, based on Beta wavelets, for 1-D and 2-D function approximation. Our purpose is to approximate an unknown function f: Rn â R from scattered samples (xi; yi = f(x)) i=1.âŚn, where:
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we have little a priori knowledge on the unknown function f which lives in some infinite dimensional smooth function space,
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the function approximation process is performed iteratively: each new measure on the function (xi; f(xi)) is used to compute a new estimate \({\hat f}\) as an approximation of the function f.
Simulation results are demonstrated to validate the generalization ability and efficiency of the proposed Beta wavelet network.
REGIM: Research Group on Intelligent Machines
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Bellil, W., Amar, C.B., Alimi, A.M. (2005). Beta wavelet networks for function approximation. In: Ribeiro, B., Albrecht, R.F., Dobnikar, A., Pearson, D.W., Steele, N.C. (eds) Adaptive and Natural Computing Algorithms. Springer, Vienna. https://doi.org/10.1007/3-211-27389-1_5
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DOI: https://doi.org/10.1007/3-211-27389-1_5
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