Rough sets in the neuro-fuzzy architectures based on non-monotonic fuzzy implications

Abstract

In this paper we presented a general solution to compose rough-neuro-fuzzy architectures. The fuzzy system in the case of missing features is derived without the assumption that used fuzzy implication is monotonic. The proposed solution is also suitable for the monotonic fuzzy implications satisfying Fodor's lemma. The architecture based on the Zadeh and Willmott fuzzy implications is derived as the special case of the proposed general solution.