Surface approximation using the 2D FFENN architecture

S. Panagopolous, J.J. Soraghan

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)2696-2704
Number of pages8
JournalEURASIP Journal on Advances in Signal Processing
Issue number17
Publication statusPublished - Mar 2004


  • neural networks
  • sea clutter
  • surface modelling
  • signal processing


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