In this study we are concerned with a new category of logic connectives and logic neurons based on the concept of uninorms. Uninorms are a generalization of t-norms and t-conorms used for composing fuzzy sets. We discuss the development of such constructs by using genetic algorithms. In this way we optimize a suite of parameters encountered in uninorms, especially their identity element. In the sequel, we introduce a class of logic neurons based on uninorms (which will be refereed to as unineurons). The learning issues of the neurons are presented and some experimental results obtained for synthetic and benchmark data are reported.
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