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Proceedings Paper

Modular neural net architecture for automatic target recognition
Author(s): Shulin Yang; Kuo-Chu Chang
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Paper Abstract

Multilayer perceptrons (MLP) have been widely applied to pattern recognition. It is found that when the data has a multi-modal distribution, a standard MLP is apt to local minima and a valid neural net classifier is difficult to obtain. In this paper, we propose a two-phase learning modular (TLM) neural net architecture to tackle the local minimum problem. The basic idea is to transform the multi- modal distribution into a known and more learnable distribution before using a global MLP to classify the data. We applied the TLM to the inverse synthetic aperture radar (ISAR) automatic target recognition (ATR), and compared its performance with that of the MLP. Experiments show that the MLP's learning often leads to a fatal minimum if its net size or the initial point is not chosen properly. Its performance depends strongly on the number of training samples as well as the architecture parameters. On the other hand, the TLM is much easier to train and can yield good recognition accuracy, at least comparable to that of the MLP. In addition, the TLM's performance is more robust.

Paper Details

Date Published: 14 June 1996
PDF: 12 pages
Proc. SPIE 2755, Signal Processing, Sensor Fusion, and Target Recognition V, (14 June 1996); doi: 10.1117/12.243158
Show Author Affiliations
Shulin Yang, George Mason Univ. (United States)
Kuo-Chu Chang, George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 2755:
Signal Processing, Sensor Fusion, and Target Recognition V
Ivan Kadar; Vibeke Libby, Editor(s)

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