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http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1499| Initialisation and training procedures for wavelet networks applied to chaotic time series | |
| VICENTE ALARCON AQUINO OLEG STAROSTENKO BASARAB JUAN MANUEL RAMIREZ CORTES MARIA DEL PILAR GOMEZ GIL EDGAR SALOMON GARCIA TREVIÑO | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| Wavelet networks Wavelets Approximation theory Multi-resolution analysis Chaotic time series | |
| Wavelet networks are a class of neural network that take advantage of good localization properties of multi-resolution analysis and combine them with the approximation abilities of neural networks. This kind of networks uses wavelets as activation functions in the hidden layer and a type of back-propagation algorithm is used for its learning. However, the training procedure used for wavelet networks is based on the idea of continuous differentiable wavelets and some of the most powerful and used wavelets do not satisfy this property. In this paper we report an algorithm for initialising and training wavelet networks applied to the approximation of chaotic time series. The proposed algorithm which has its foundations on correlation analysis of signals allows the use of different types of wavelets, namely, Daubechies, Coiflets, and Symmlets. To show this, comparisons are made for chaotic time series approximation between the proposed approach and the typical wavelet network. | |
| CRL Publishing Ltd | |
| 2010 | |
| Artículo | |
| Inglés | |
| Estudiantes Investigadores Público en general | |
| Alarcon-Aquino, V., et al., (2010). Initialisation and training procedures for wavelet networks applied to chaotic time series, Engineering Intelligent Systems, Vol. 1 (1): 1-9 | |
| ELECTRÓNICA | |
| Versión aceptada | |
| acceptedVersion - Versión aceptada | |
| Aparece en las colecciones: | Artículos de Electrónica |
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