Initialisation and training procedures for wavelet networks applied to chaotic time series

dc.creatorVICENTE ALARCON AQUINO
dc.creatorOLEG STAROSTENKO BASARAB
dc.creatorJUAN MANUEL RAMIREZ CORTES
dc.creatorMARIA DEL PILAR GOMEZ GIL
dc.creatorEDGAR SALOMON GARCIA TREVIÑO
dc.date2010
dc.date.accessioned2023-07-25T16:23:47Z
dc.date.available2023-07-25T16:23:47Z
dc.identifierhttp://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1499
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7806693
dc.descriptionWavelet 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.
dc.formatapplication/pdf
dc.languageeng
dc.publisherCRL Publishing Ltd
dc.relationcitation: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
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectinfo:eu-repo/classification/Wavelet networks/Wavelet networks
dc.subjectinfo:eu-repo/classification/Wavelets/Wavelets
dc.subjectinfo:eu-repo/classification/Approximation theory/Approximation theory
dc.subjectinfo:eu-repo/classification/Multi-resolution analysis/Multi-resolution analysis
dc.subjectinfo:eu-repo/classification/Chaotic time series/Chaotic time series
dc.subjectinfo:eu-repo/classification/cti/1
dc.subjectinfo:eu-repo/classification/cti/22
dc.subjectinfo:eu-repo/classification/cti/2203
dc.subjectinfo:eu-repo/classification/cti/2203
dc.titleInitialisation and training procedures for wavelet networks applied to chaotic time series
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.audiencestudents
dc.audienceresearchers
dc.audiencegeneralPublic


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