Self-growing neural network architecture using crisp and fuzzy entropy | Zendy

Krzysztof J. Cios | Zendy

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<title>Self-growing neural network architecture using crisp and fuzzy entropy</title>

Author(s) -

Krzysztof J. Cios

Publication year - 1992

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.140154

Subject(s) - computer science , artificial neural network , feedforward neural network , entropy (arrow of time) , architecture , benchmark (surveying) , artificial intelligence , ribbon , feed forward , fuzzy logic , time delay neural network , pattern recognition (psychology) , algorithm , mathematics , art , physics , geometry , geodesy , quantum mechanics , control engineering , engineering , visual arts , geography

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.

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