Self-growing neural network architecture using crisp and fuzzy entropy

Krzysztof J. Cios

doi:10.1117/12.140154

1 July 1992 Self-growing neural network architecture using crisp and fuzzy entropy

Krzysztof J. Cios

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140154
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

The paper briefly describes the self-growing neural network algorithm, CID3, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results for a real-life recognition problem of distinguishing defects in a glass ribbon, and for a benchmark problem of telling two spirals apart are shown and discussed.

Citation Download Citation

Krzysztof J. Cios "Self-growing neural network architecture using crisp and fuzzy entropy", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140154

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