Constrained neural network architectures for target recognition

Donald R. Hush; Mary M. Moya; Shang-Ying Clark

doi:10.1117/12.139976

16 September 1992 Constrained-neural-network architectures for target recognition

Donald R. Hush, Mary M. Moya, Shang-Ying Clark

Proceedings Volume 1709, Applications of Artificial Neural Networks III; (1992) https://doi.org/10.1117/12.139976
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

This paper describes several different types of constraints that can be placed on multilayered feedforward neural networks which are used for automatic target recognition (ATR). We show how unconstrained networks are likely to give poor generalization on the ATR problem. We also show how the ATR problem requires a special type of classifier called a one-class classifier. The network constraints come in two forms: architectural constraints and learning constraints. Some of the constraints are used to improve generalization, while others are incorporated so that the network will be forced to perform one-class classification.

Citation Download Citation

Donald R. Hush, Mary M. Moya, and Shang-Ying Clark "Constrained-neural-network architectures for target recognition", Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); https://doi.org/10.1117/12.139976

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