Surface Approximation Using the 2D FFENN Architecture
<p/> <p>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 tech...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2004-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1155/S111086570440612X |
| _version_ | 1818525459963445248 |
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| author | Panagopoulos S Soraghan JJ |
| author_facet | Panagopoulos S Soraghan JJ |
| author_sort | Panagopoulos S |
| collection | DOAJ |
| description | <p/> <p>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.</p> |
| first_indexed | 2024-12-11T06:09:31Z |
| format | Article |
| id | doaj.art-45211725c04b4c1dbc5f0619b4d71fc6 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-11T06:09:31Z |
| publishDate | 2004-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-45211725c04b4c1dbc5f0619b4d71fc62022-12-22T01:18:11ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802004-01-01200417348702Surface Approximation Using the 2D FFENN ArchitecturePanagopoulos SSoraghan JJ<p/> <p>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.</p>http://dx.doi.org/10.1155/S111086570440612Xneural networkssea cluttersurface modeling |
| spellingShingle | Panagopoulos S Soraghan JJ Surface Approximation Using the 2D FFENN Architecture EURASIP Journal on Advances in Signal Processing neural networks sea clutter surface modeling |
| title | Surface Approximation Using the 2D FFENN Architecture |
| title_full | Surface Approximation Using the 2D FFENN Architecture |
| title_fullStr | Surface Approximation Using the 2D FFENN Architecture |
| title_full_unstemmed | Surface Approximation Using the 2D FFENN Architecture |
| title_short | Surface Approximation Using the 2D FFENN Architecture |
| title_sort | surface approximation using the 2d ffenn architecture |
| topic | neural networks sea clutter surface modeling |
| url | http://dx.doi.org/10.1155/S111086570440612X |
| work_keys_str_mv | AT panagopouloss surfaceapproximationusingthe2dffennarchitecture AT soraghanjj surfaceapproximationusingthe2dffennarchitecture |