Surface approximation using the 2D FFENN architecture

S. Panagopolous; J.J. Soraghan

doi:10.1155/S111086570440612X

Surface approximation using the 2D FFENN architecture

S. Panagopolous, J.J. Soraghan

Electronic And Electrical Engineering

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	2696-2704
Number of pages	8
Journal	EURASIP Journal on Advances in Signal Processing
Volume	2004
Issue number	17
DOIs	https://doi.org/10.1155/S111086570440612X
Publication status	Published - Mar 2004

Keywords

neural networks
sea clutter
surface modelling
signal processing

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10.1155/S111086570440612X

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title = "Surface approximation using the 2D FFENN architecture",

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.",

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author = "S. Panagopolous and J.J. Soraghan",

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AU - Soraghan, J.J.

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AB - 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.

KW - neural networks

KW - sea clutter

KW - surface modelling

KW - signal processing

UR - http://dx.doi.org/10.1155/S111086570440612X

U2 - 10.1155/S111086570440612X

DO - 10.1155/S111086570440612X

M3 - Article

SN - 1110-8657

VL - 2004

SP - 2696

EP - 2704

JO - EURASIP Journal on Advances in Signal Processing

JF - EURASIP Journal on Advances in Signal Processing

IS - 17

ER -

Surface approximation using the 2D FFENN architecture

Abstract

Keywords

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