Optimal design of hierarchical wavelet networks for time-series forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  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=",
  size =         "6 pages",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  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