Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation

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 pro...

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Main Authors:	Loo, C.K., Law, A., Lim, W.S., Rao, M.V.C.
Format:	Article
Published:	2006
Subjects:	T Technology (General)
Online Access:	http://download.springer.com/static/pdf/775/art%253A10.1007%252Fs00521-005-0010-1.pdf?auth66=1352708401_338c8b84c0323ac65ee4ad8270b8185f&ext=.pdf http://download.springer.com/static/pdf/775/art%253A10.1007%252Fs00521-005-0010-1.pdf?auth66=1352708401_338c8b84c0323ac65ee4ad8270b8185f&ext=.pdf

id	um-5175
recordtype	eprints
spelling	um-51752013-03-21T01:30:51Z Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation Loo, C.K. Law, A. Lim, W.S. Rao, M.V.C. T Technology (General) 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. 2006 Article PeerReviewed http://download.springer.com/static/pdf/775/art%253A10.1007%252Fs00521-005-0010-1.pdf?auth66=1352708401_338c8b84c0323ac65ee4ad8270b8185f&ext=.pdf Loo, C.K.; Law, A.; Lim, W.S.; Rao, M.V.C. (2006) Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation. Neural Computing & Applications <http://eprints.um.edu.my/view/publication/Neural_Computing_=26_Applications.html>, 15 (1). pp. 79-90. ISSN 0941-0643 http://eprints.um.edu.my/5175/
repository_type	Digital Repository
institution_category	Local University
institution	University Malaya
building	UM Research Repository
collection	Online Access
topic	T Technology (General)
spellingShingle	T Technology (General) Loo, C.K. Law, A. Lim, W.S. Rao, M.V.C. Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation
description	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.
format	Article
author	Loo, C.K. Law, A. Lim, W.S. Rao, M.V.C.
author_facet	Loo, C.K. Law, A. Lim, W.S. Rao, M.V.C.
author_sort	Loo, C.K.
title	Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation
title_short	Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation
title_full	Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation
title_fullStr	Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation
title_full_unstemmed	Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation
title_sort	probabilistic ensemble simplified fuzzy artmap for sonar target differentiation
publishDate	2006
url	http://download.springer.com/static/pdf/775/art%253A10.1007%252Fs00521-005-0010-1.pdf?auth66=1352708401_338c8b84c0323ac65ee4ad8270b8185f&ext=.pdf http://download.springer.com/static/pdf/775/art%253A10.1007%252Fs00521-005-0010-1.pdf?auth66=1352708401_338c8b84c0323ac65ee4ad8270b8185f&ext=.pdf
first_indexed	2018-09-05T16:50:02Z
last_indexed	2018-09-05T16:50:02Z
_version_	1610833550816313344

Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation

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