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Proceedings Paper

Robust classification of variable-length sonar sequences
Author(s): Joydeep Ghosh; Narsimham V. Gangishetti; Srinivasa V. Chakravarthy
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Paper Abstract

Two types of artificial neural networks are introduced for the robust classification of spatio- temporal sequences. The first network is the Adaptive Spatio-Temporal Recognizer (ASTER), which adaptively estimates the confidence that a (variable length) signal of a known class is present by continuously monitoring a sequence of feature vectors. If the confidence for any class exceeds a threshold value at some moment, the signal is considered to be detected and classified. The nonlinear behavior of ASTER provides more robust performance than the related dynamic time warping algorithm. ASTER is compared with a more common approach wherein a self-organizing feature map is first used to map a sequence of extracted feature vectors onto a lower dimensional trajectory, which is then identified using a variant of the feedforward time delay neural network. The performance of these two networks is compared using artificial sonograms as well as feature vectors strings obtained from short-duration oceanic signals.

Paper Details

Date Published: 2 September 1993
PDF: 12 pages
Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152524
Show Author Affiliations
Joydeep Ghosh, Univ. of Texas/Austin (United States)
Narsimham V. Gangishetti, Univ. of Texas/Austin (United States)
Srinivasa V. Chakravarthy, Univ. of Texas/Austin (United States)

Published in SPIE Proceedings Vol. 1965:
Applications of Artificial Neural Networks IV
Steven K. Rogers, Editor(s)

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