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A Comparison of Self-organizing Neural Networks for Fast Clustering of Radar Pulses by Eric Granger; Yvon Savaria; Pierre Lavoie; Marc-André Cantin is a Computer Science article available to read on EtoBox.

What is A Comparison of Self-organizing Neural Networks for Fast Clustering of Radar Pulses about?

Four self-organizing neural networks are compared for automatic deinterleaving of radar pulse streams in electronic warfare systems. The neural networks are the Fuzzy Adaptive Resonance Theory, Fuzzy Min-Max Clustering, Integrated Adaptive Fuzzy Clustering, and Self-Organizing Feature Mapping. Given the need for a clustering procedure that offers both accurate results and computational efficiency, these four networks are examined from three perspectives clustering quality, convergence time, and computational complexity. The clustering quality and convergence time are measured via computer simulation, using a set of radar pulses collected in the field. Estimation of the worst-case running time for each network allows for the assessment of computational complexity. The effect of the pattern presentation order is analyzed by presenting the data not just in random order, but also in radar-like orders called burst and interleaved.

Who reads A Comparison of Self-organizing Neural Networks for Fast Clustering of Radar Pulses?

Author

Eric Granger; Yvon Savaria; Pierre Lavoie; Marc-André Cantin

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0165-1684)

Published

1998

Language

EN

Field

Computer Science (Physical Sciences)