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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Fuzzy Implications Generating from Fuzzy Negations
verfasst von : Georgios Souliotis, Basil Papadopoulos
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2018
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Abstract
A basic building block in the foundation of fuzzy neural networks is the theory of fuzzy implications. Fuzzy implications play a crucial role in this topic. The aim of this paper is to find a new method of generating fuzzy implications. based on a given fuzzy negation. Specifically, we propose using a given fuzzy negation and a function so as to generate rules of fuzzy implications, that is rules which regulate decision making, thus adapting mathematics to human common sense. A great advantage of this construction is that the implications generated in this way fulfil many axioms and serious properties among the set of required ones.