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EnglishThe 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.
| Titolo: | Uninorm Based Fuzzy Network for Tree Data Structures | |
| Autori: | ||
| Data di pubblicazione: | 2009 | |
| Rivista: | ||
| 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. | |
| Handle: | http://hdl.handle.net/11367/24846 | |
| ISBN: | 3642022812 | |
| Appare nelle tipologie: | 2.1 Contributo in volume (Capitolo o Saggio) |
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