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Proceedings Paper

Fuzzy clustering with a specified membership function for target detection with a radar system
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Paper Abstract

Many common clustering algorithms, such as the fuzzy C-means and the classical k-means clustering algorithms, proceed without making any assumptions about the form of the detector that will use the parameters that they determine. We compare the performance of a radial basis function (RBF) network with parameters that are determined using a modified fuzzy clustering procedure to that of an RBF network with parameters that are determined using a least-mean-square- error (classical) clustering procedure. As part of the fuzzy clustering procedure, we assume a particular functional form for the fuzzy membership function. We train and test both of the networks on simulated data and present performance results in the form of receiver operating characteristic curves.

Paper Details

Date Published: 27 August 2001
PDF: 7 pages
Proc. SPIE 4382, Algorithms for Synthetic Aperture Radar Imagery VIII, (27 August 2001); doi: 10.1117/12.438212
Show Author Affiliations
Kenneth I. Ranney, Army Research Lab. (United States)
Hiralal Khatri, Army Research Lab. (United States)
Lam H. Nguyen, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 4382:
Algorithms for Synthetic Aperture Radar Imagery VIII
Edmund G. Zelnio, Editor(s)

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