Document Type


Publication Date



Machine learning, Fuzzy logic, Decomposition method, Many-valued logic -- Mathematical models


This paper presents a new approach to decomposition of fuzzy functions. A tutorial background on fuzzy logic representations is first given to emphasize next the simplicity and generality of this new approach. Ashenhurst-like decomposition of fuzzy functions was discussed in [3] but it was not suitable for programming and was not programmed. In our approach, fuzzy functions are converted to multiple-valued functions and decomposed using an mv decomposer. Then the decomposed multiple-valued functions are converted back to fuzzy functions. This approach allows for Curtis-like decompositions with arbitrary number of intermediate fuzzy variables, that have been not presented for fuzzy functions by the previous authors. Extension of the method to fuzzy relations is also shown. The new approach is suitable for Machine Learning.


Originally presented at the International Conference on Fuzzy Information Processing. Theories and Applications, held in Beijing, China, in 2003, and subsequently included in its proceedings.

Persistent Identifier