Optimal design of hierarchical wavelet networks for time-series forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Chen:2006:ESANN,

• author = "Yuehui Chen and Bo Yang and Ajith Abraham",
• title = "Optimal design of hierarchical wavelet networks for time-series forecasting",
• booktitle = "14th European Symposium on Artificial Neural Networks (ESANN 2006)",
• year = "2006",
• pages = "155--160",
• address = "Bruges, Belgium",
• month = apr # " 26-28",
• keywords = "genetic algorithms, genetic programming, ECGP",
• isbn13 = "2-930307-06-4",
• URL = "http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-57.pdf",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.9044",
• size = "6 pages",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• language = "en",
• oai = "oai:CiteSeerXPSU:10.1.1.136.9044",
• 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