Title :
Buried Tag Identification with a new RBF Classifier
Author :
Beheim, L. ; Zitouni, A. ; Belloir, F.
Author_Institution :
CReSTIC, Univ. of Reims Champagne-Ardenne
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;
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
DOI :
10.1109/NORSIG.2006.275215
