Uninorm Based Fuzzy Network for Tree Data Structures

Authors:
Angelo Ciaramella;Witold Pedrycz;Alfredo Petrosino
Affiliations:
Dept. of Applied Sciences, University of Naples "Parthenope", Napoli, Italy I-80143;Dept. of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada;Dept. of Applied Sciences, University of Naples "Parthenope", Napoli, Italy I-80143
Venue:
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Year:
2009

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Abstract

The aim of this study is to introduce a fuzzy model to process structured data. A structured organization of information is typically required by symbolic processing. Most connectionist models assume that data are organized in a form of relatively simple structures such as vectors or sequences. In this work, we propose a connectionist model that can directly process labeled trees. The model is based on a new category of logic connectives and logic neurons that use the concept of uninorms. Uninorms are a generalization of t -norms and t -conorms used for aggregating fuzzy sets. Using a back-propagation algorithm we optimize the parameters of the model (relations and membership functions). The learning issues are presented and some experimental results obtained for synthetic realistic data, are reported.