Surface approximation using the multi-level and interpolation 2D FFENN

S. Panagopolous, J.J. Soraghan

Research output: Contribution to conferencePaper

Abstract

In this paper, we present the development of a two dimensional feed-forward functionally expanded neural network (2D FFENN) surface modeler. Two application models of the generic design are also proposed. The scope of this work is the development of a two-dimensional system able to produce surface data mappings. The main application area of interest is sea surface modeling and target detection by sea clutter suppression. New nonlinear multi-level 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. Results for surface mappings generated by the multi-level 2D FFENN and interpolation 2D FFENN designs are presented.
Original languageEnglish
Publication statusPublished - 2002
EventSignal Processing, Pattern Recognition, and Applications (SPPRA 2002) - Crete, Greece
Duration: 25 Jun 200228 Jun 2002

Conference

ConferenceSignal Processing, Pattern Recognition, and Applications (SPPRA 2002)
CountryGreece
CityCrete
Period25/06/0228/06/02

Fingerprint

Interpolation
Target tracking
Neural networks

Keywords

  • neural networks
  • surface modeling
  • sea clutter
  • radar
  • surface approximation
  • multi-level
  • interpolation
  • 2d ffenn

Cite this

Panagopolous, S., & Soraghan, J. J. (2002). Surface approximation using the multi-level and interpolation 2D FFENN. Paper presented at Signal Processing, Pattern Recognition, and Applications (SPPRA 2002) , Crete, Greece.
Panagopolous, S. ; Soraghan, J.J. / Surface approximation using the multi-level and interpolation 2D FFENN. Paper presented at Signal Processing, Pattern Recognition, and Applications (SPPRA 2002) , Crete, Greece.
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title = "Surface approximation using the multi-level and interpolation 2D FFENN",
abstract = "In this paper, we present the development of a two dimensional feed-forward functionally expanded neural network (2D FFENN) surface modeler. Two application models of the generic design are also proposed. The scope of this work is the development of a two-dimensional system able to produce surface data mappings. The main application area of interest is sea surface modeling and target detection by sea clutter suppression. New nonlinear multi-level 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. Results for surface mappings generated by the multi-level 2D FFENN and interpolation 2D FFENN designs are presented.",
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author = "S. Panagopolous and J.J. Soraghan",
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note = "Signal Processing, Pattern Recognition, and Applications (SPPRA 2002) ; Conference date: 25-06-2002 Through 28-06-2002",

}

Panagopolous, S & Soraghan, JJ 2002, 'Surface approximation using the multi-level and interpolation 2D FFENN' Paper presented at Signal Processing, Pattern Recognition, and Applications (SPPRA 2002) , Crete, Greece, 25/06/02 - 28/06/02, .

Surface approximation using the multi-level and interpolation 2D FFENN. / Panagopolous, S.; Soraghan, J.J.

2002. Paper presented at Signal Processing, Pattern Recognition, and Applications (SPPRA 2002) , Crete, Greece.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Surface approximation using the multi-level and interpolation 2D FFENN

AU - Panagopolous, S.

AU - Soraghan, J.J.

PY - 2002

Y1 - 2002

N2 - In this paper, we present the development of a two dimensional feed-forward functionally expanded neural network (2D FFENN) surface modeler. Two application models of the generic design are also proposed. The scope of this work is the development of a two-dimensional system able to produce surface data mappings. The main application area of interest is sea surface modeling and target detection by sea clutter suppression. New nonlinear multi-level 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. Results for surface mappings generated by the multi-level 2D FFENN and interpolation 2D FFENN designs are presented.

AB - In this paper, we present the development of a two dimensional feed-forward functionally expanded neural network (2D FFENN) surface modeler. Two application models of the generic design are also proposed. The scope of this work is the development of a two-dimensional system able to produce surface data mappings. The main application area of interest is sea surface modeling and target detection by sea clutter suppression. New nonlinear multi-level 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. Results for surface mappings generated by the multi-level 2D FFENN and interpolation 2D FFENN designs are presented.

KW - neural networks

KW - surface modeling

KW - sea clutter

KW - radar

KW - surface approximation

KW - multi-level

KW - interpolation

KW - 2d ffenn

M3 - Paper

ER -

Panagopolous S, Soraghan JJ. Surface approximation using the multi-level and interpolation 2D FFENN. 2002. Paper presented at Signal Processing, Pattern Recognition, and Applications (SPPRA 2002) , Crete, Greece.