Surface approximation using the 2D FFENN architecture

Panagopolous, S. and Soraghan, J.J. (2004) Surface approximation using the 2D FFENN architecture. EURASIP Journal on Advances in Signal Processing, 2004 (17). pp. 2696-2704. ISSN 1110-8657 (http://dx.doi.org/10.1155/S111086570440612X)

Abstract

A new two-dimensional feed-forward functionally expanded neural network (2D FFENN) used to produce surface models in two dimensions is presented. New nonlinear multilevel surface basis functions are proposed for the network's functional expansion. A network optimization technique based on an iterative function selection strategy is also described. Comparative simulation results for surface mappings generated by the 2D FFENN, multilevel 2D FFENN, multilayered perceptron (MLP), and radial basis function (RBF) architectures are presented.

ORCID iDs

Soraghan, J.J. ORCID: https://orcid.org/0000-0003-4418-7391

• Share and Export
  

  RDF+XMLBibTeXRIOXX2 XMLRDF+N-TriplesJSONDublin CoreAtomSimple MetadataReferMETSHTML CitationASCII CitationOpenURL ContextObjectEndNoteOpenURL ContextObject in SpanMODSMPEG-21 DIDLEP3 XMLData Cite XMLRIS (EndNote Online, Zotero, Ref manager, etc)RDF+N3Multiline CSV
• Item metadata

  Item type:	Article
  ID code:	7115
  Dates:	Date Event March 2004 Published
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering
  Depositing user:	Strathprints Administrator
  Date deposited:	05 Nov 2008
  Last modified:	11 Nov 2024 08:43
  URI:	https://strathprints.strath.ac.uk/id/eprint/7115