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Proceedings Paper

Multiple-class identification algorithm using genetic neural networks
Author(s): Rustom Mamlook; Wiley E. Thompson
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Paper Abstract

Multiple-class identification algorithm using genetic neural networks is presented. The algorithm uses a feedforward neural network so it is fast. The algorithm uses the Kohonen network to provide an unsupervised learning. The Kohonen network is used with Z-axis normalization. The weight initialization is done by genetic optimization to escape from local minima. The performance of the algorithm is evaluated using a confusion matrix method. The algorithm does not require the number of classes to be known a priori. It also provides a threshold selection method. An example is given to illustrate the application of the algorithm and to evaluate its performance.

Paper Details

Date Published: 5 July 1995
PDF: 8 pages
Proc. SPIE 2484, Signal Processing, Sensor Fusion, and Target Recognition IV, (5 July 1995); doi: 10.1117/12.213064
Show Author Affiliations
Rustom Mamlook, Philadelphia Univ. (Jordan)
Wiley E. Thompson, New Mexico State Univ. (United States)


Published in SPIE Proceedings Vol. 2484:
Signal Processing, Sensor Fusion, and Target Recognition IV
Ivan Kadar; Vibeke Libby, Editor(s)

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