Abstract
In this paper we present algorithms which are adaptive and based on neural networks and wavelet series to build wavenets function approximators. Results are shown in numerical simulation of two wavenets approximators architectures: the first is based on a wavenet for approach the signals under study where the parameters of the neural network are adjusted online, the other uses a scheme approximators with an IIR filter in the output of wavenet, which helps to reduce convergence time to a minimum time desired.
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Domínguez Mayorga, C.R., Espejel Rivera, M.A., Ramos Velasco, L.E., Ramos Fernández, J.C., Escamilla Hernández, E. (2011). Wavelet Neural Network Algorithms with Applications in Approximation Signals. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_33
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DOI: https://doi.org/10.1007/978-3-642-25330-0_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25329-4
Online ISBN: 978-3-642-25330-0
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