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Prediction of polymer optical fiber properties using artificial neural networks

Abstract:

Polymer fibers are finding increasing applications in commercial optical communication systems. Polymer optical fibers, with specified desirable consumer characteristics, can be computationally designed. Through the use of an extensive structure - property correlation database, properties of polymers can be predicted by a Neural Network. In this paper we are focusing on glass transition temperature (Tg) that influences a desired outcome in polymeric optical fibers. Performance of such fibers ...

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Publisher copy:
10.1109/CIMSA.2007.4362530

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Journal:
Proceedings of the 2007 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA
Pages:
14-18
Publication date:
2007-01-01
DOI:
URN:
uuid:58153fa7-6f6e-452d-abf9-fa353b8c5035
Source identifiers:
331929
Local pid:
pubs:331929
Language:
English
Keywords:

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