Title :
Evolving Radial Basis Function Neural Network with Hausdorff Similarity Measure for SONAR signals detection/ classification
Author_Institution :
Sci. Appl. Telecommun. Coll., Tehran, Iran
Abstract :
In this paper, a new approach has been proposed for detection/ classification of SONAR signals based on radial basis function neural network (RBFNN), which has been modified with a robust and reliable measure named: Hausdorff similarity measure (HSM). Methodologies of approach and simulation results are also represented. The final results show the new approach is able to increase the total performance of detection/ classification of SONAR targets even in low SNR.
Keywords :
geophysical signal processing; radial basis function networks; signal classification; sonar detection; sonar signal processing; Hausdorff similarity measure; RBFNN; SONAR signal classification; SONAR signal detection; radial basis function neural network; Kernel; Neural networks; Neurons; Radial basis function networks; Robustness; Signal detection; Sonar applications; Sonar detection; Sonar measurements; Testing; Classification; Detection; Hausdorff; Neural Network; SONAR;
Conference_Titel :
OCEANS 2009 - EUROPE
Conference_Location :
Bremen
Print_ISBN :
978-1-4244-2522-8
Electronic_ISBN :
978-1-4244-2523-5
DOI :
10.1109/OCEANSE.2009.5278146
