Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation

Loo, Chu Kiong and Law, A. and Lim, W.S. and Rao, M.V.C. (2006) Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation. Neural Computing and Applications, 15 (1). pp. 79-90. ISSN 0941-0643,

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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.

Item Type: Article
Additional Information: Loo, CK Law, A Lim, WS Rao, MVC
Uncontrolled Keywords: Target differentiation; Neural computation; Intelligent robot; Ordering; Optimal solution; Cross validation; Benchmarks; Classifier; Object recognition; Neural network; Signal processing; Sonar
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology > Department of Artificial Intelligence
Depositing User: Miss Nur Jannatul Adnin Ahmad Shafawi
Date Deposited: 21 Mar 2013 01:30
Last Modified: 16 Jan 2020 01:53

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