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Proceedings Paper

Structurally adaptive neural network for underwater target classification
Author(s): Qiang Huang; Mahmood R. Azimi-Sadjadi; Sassan Sheedvash
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Paper Abstract

This paper presents the application of a novel scheme for dynamic structural adaptation for back-propagation neural networks. It utilizes the time and order update formulations in the orthogonal projection theorem to establish a recursive weight updating procedure for the training process and a dynamic node creation procedure during the training process. The effectiveness of the algorithm is demonstrated on a simple multiplexer problem and a real-life application dealing with underwater target classification from the acoustic backscattered signals. It is shown through the simulation results that the dynamic structural adaptation scheme offers better trainability for the networks without requiring prohibitive cost of retraining. In addition, the results on the testing data indicate good classification performance of the network trained in conjunction with the structural adaptation method.

Paper Details

Date Published: 4 September 1998
PDF: 11 pages
Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); doi: 10.1117/12.324206
Show Author Affiliations
Qiang Huang, Colorado State Univ. (United States)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)
Sassan Sheedvash, IBM Corp. (United States)

Published in SPIE Proceedings Vol. 3392:
Detection and Remediation Technologies for Mines and Minelike Targets III
Abinash C. Dubey; James F. Harvey; J. Thomas Broach, Editor(s)

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