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Proceedings Paper

RBF iterative construction algorithm (RICA)
Author(s): Terry A. Wilson; Steven K. Rogers; Mark E. Oxley; Thomas F. Rathbun; Martin P. DeSimio; Matthew Kabrisky
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Paper Abstract

A Radial Basis Function (RBF) Iterative Construction Algorithm (RICA) is presented that autonomously determines the size of the network architecture needed to perform classification on a given data set. The algorithm uses a combination of a Gaussian goodness-of-fit measure and Mahalanobis distance clustering to calculate the number of hidden nodes needed and to estimate the parameters of the hidden node basis functions. An iterative minimum squared error reduction method is used to optimize the output layer weights. RICA is compared to several neural network algorithms, including a fixed architecture multilayer perceptron (MLP), a fixed architecture RBF, and an adaptive architecture MLP, using optical character recognition and infrared image data.

Paper Details

Date Published: 25 March 1998
PDF: 8 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304831
Show Author Affiliations
Terry A. Wilson, Air Force Institute of Technology (United States)
Steven K. Rogers, Battelle Memorial Institute (United States)
Mark E. Oxley, Wright Patterson Air Force Base (United States)
Thomas F. Rathbun, Air Force Research Lab. (United States)
Martin P. DeSimio, Qualia Computing, Inc. (United States)
Matthew Kabrisky, Air Force Institute of Technology (United States)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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