Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation
Document Type
Article
Publication Date
1-1-2006
Abstract
This Study investigates the processing of sonar signals with ensemble neural networks for robust recognition of simple objects such as plane, corner and trapezium surface. The ensemble neural networks can differentiate the target objects with high accuracy. The simplified fuzzy ARTMAP (SFAM) and probabilistic ensemble simplified fuzzy ARTMAP (PESFAM) are compared in terms of classification accuracy. The PESFAM implements an accurate and effective probabilistic plurality voting method to combine outputs from multiple SFAM classifiers. Five benchmark data sets have been used to evaluate the applicability of the proposed ensemble SFAM network. The PESFAM achieves good accuracy based on the twofold cross-validation results. In addition, the effectiveness of the proposed ensemble SFAM is delineated in sonar target differentiation. The experiments demonstrate the potential of PESFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent classification tool in mobile robot application.
Keywords
Target differentiation, Neural computation, Intelligent robot, Ordering, Optimal solution, Cross validation, Benchmarks, Classifier, Object recognition, Neural network, Signal processing, Sonar
Divisions
ai
Publication Title
Neural Computing and Applications
Volume
15
Issue
1
Publisher
Springer Verlag (Germany)
Additional Information
Loo, CK Law, A Lim, WS Rao, MVC