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Wavelet Neural Networks: A Practical Guide

Alexandridis, Antonis, Zapranis, Achilleas (2013) Wavelet Neural Networks: A Practical Guide. Neural Networks, 42 . pp. 1-27. ISSN 0893-6080. (doi:10.1016/j.neunet.2013.01.008)

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http://dx.doi.org/10.1016/j.neunet.2013.01.008

Abstract

Wavelet networks (WNs) are a new class of networks which have been used with great success in a wide range of application. However a general accepted framework for applying WNs is missing from the literature. In this study, we present a complete statistical model identification framework in order to apply WNs in various applications. The following subjects were thorough examined: the structure of a WN, training methods, initialization algorithms, variable significance and variable selection algorithms, model selection methods and finally methods to construct confidence and prediction intervals. In addition the complexity of each algorithm is discussed. Our proposed framework was tested in two simulated cases, in one chaotic time series described by the Mackey-Glass equation and in three real datasets described by daily temperatures in Berlin, daily wind speeds in New York and breast cancer classification. Our results have shown that the proposed algorithms produce stable and robust results indicating that our proposed framework can be applied in various applications.

Item Type: Article
DOI/Identification number: 10.1016/j.neunet.2013.01.008
Uncontrolled keywords: Wavelet networks; model identification; variable selection; model selection; confidence intervals; prediction intervals
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Actuarial Science
Faculties > Social Sciences > Kent Business School > Accounting and Finance
Depositing User: Antonis Alexandridis
Date Deposited: 14 Jan 2013 11:06 UTC
Last Modified: 29 May 2019 09:56 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/32961 (The current URI for this page, for reference purposes)
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