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Proceedings Paper

Unsupervised/supervised hybrid networks for identification of TSS-1 satellite
Author(s): Zhiling Wang; Andrea Guerriero; Marco De Sario; S. Losito
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Paper Abstract

Neural networks have potential advantages such as real-time operation and robustness based on their parallel structure, self-organization, fuzziness, and particularly their adaptive learning ability. A single neural network is useful for identification of objects. To carry out identifying complex objects, however, it is necessary to consider hybrid architectures of two or more networks, which offer some degrees of improvement in performances. In this paper, neural learning techniques, the self-organizing feature mapping (SOFM), and learning vector quantization (LVQ2) have been applied to the automatic target recognition problem in the presence of a satellite object with high level noises. SOFM, unsupervised learning captures the homogeneity within-class characteristics; whereas LVQ2, supervised learning captures the heterogeneity of between-class.

Paper Details

Date Published: 28 August 1995
PDF: 8 pages
Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); doi: 10.1117/12.217493
Show Author Affiliations
Zhiling Wang, Italian Space Agency (Italy)
Andrea Guerriero, Univ. of Bari (Italy)
Marco De Sario, Univ. of Bari (Italy)
S. Losito, Italian Space Agency (Italy)


Published in SPIE Proceedings Vol. 2620:
International Conference on Intelligent Manufacturing
Shuzi Yang; Ji Zhou; Cheng-Gang Li, Editor(s)

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