The Genetic Development of Uninorm-Based Neurons
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
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MDAI '08 Sabadell Proceedings of the 5th International Conference on Modeling Decisions for Artificial Intelligence
Uninorm Based Fuzzy Network for Tree Data Structures
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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IEEE Transactions on Fuzzy Systems
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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International Journal of Intelligent Systems
Information Sciences: an International Journal
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In this paper, we introduce a new category of logic neurons- unineurons that are based on the concept of uninorms. As uninorms form a certain generalization of the generic categories of fuzzy set operators such as t-norms and t-conorms, the proposed unineurons inherit their logic processing capabilities which make them flexible and logically appealing. We discuss several fundamental categories of uninorms (such as UNI_or, UNI_and, and alike). In particular, we focus on the interpretability of networks composed of unineurons leading to several categories of rules to be exploited in rule-based systems. The learning aspects of the unineurons are presented along with detailed optimization schemes. Experimental results tackle two categories of problems such as: (a) a logic approximation of fuzzy sets, and (b) a design of associations between information granules where the ensuing development schemes directly relate to the fundamentals of granular (fuzzy) modeling