Optimal design of hierarchical wavelet networks for time-series forecasting
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Chen:2006:ESANN,
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author = "Yuehui Chen and Bo Yang and Ajith Abraham",
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title = "Optimal design of hierarchical wavelet networks for
time-series forecasting",
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booktitle = "14th European Symposium on Artificial Neural Networks
(ESANN 2006)",
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year = "2006",
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pages = "155--160",
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address = "Bruges, Belgium",
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month = apr # " 26-28",
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keywords = "genetic algorithms, genetic programming, ECGP",
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isbn13 = "2-930307-06-4",
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URL = "http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-57.pdf",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.9044",
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size = "6 pages",
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bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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language = "en",
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oai = "oai:CiteSeerXPSU:10.1.1.136.9044",
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abstract = "The purpose of this study is to identify the
Hierarchical Wavelet Neural Networks (HWNN) and select
important input features for each sub-wavelet neural
network automatically. Based on the predefined
instruction/operator sets, a HWNN is created and
evolved using tree-structure based Extended Compact
Genetic Programming (ECGP), and the parameters are
optimised by Differential Evolution (DE) algorithm.
This framework also allows input variables selection.
Empirical results on benchmark time-series
approximation problems indicate that the proposed
method is effective and efficient.",
- }
Genetic Programming entries for
Yuehui Chen
Bo Yang
Ajith Abraham
Citations