DocumentCode
3096000
Title
Buried Tag Identification with a new RBF Classifier
Author
Beheim, L. ; Zitouni, A. ; Belloir, F.
Author_Institution
CReSTIC, Univ. of Reims Champagne-Ardenne
fYear
2006
fDate
38869
Firstpage
150
Lastpage
153
Abstract
This article presents a new neural classifier based on an RBF network. This classifier increases relatively the recognition rate while decreasing remarkably the number of hidden layer neurons. It is very general RBF classifier, very simple, not requiring any adjustment parameter, and presenting an excellent ratio performances/neurons number. A comparative study of its performances is presented and illustrated by examples on real databases
Keywords
buried object detection; pattern classification; radial basis function networks; RBF network; buried metallic tags; buried tag identification; databases; hidden layer neuron; neural classifier; performances; radial basis function; smart eddy current sensor; Clustering algorithms; Covariance matrix; Databases; Multi-layer neural network; Multidimensional systems; Multilayer perceptrons; Neural networks; Neurons; Prototypes; Radial basis function networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Symposium, 2006. NORSIG 2006. Proceedings of the 7th Nordic
Conference_Location
Rejkjavik
Print_ISBN
1-4244-0412-6
Electronic_ISBN
1-4244-0413-4
Type
conf
DOI
10.1109/NORSIG.2006.275215
Filename
4052210
Link To Document