Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Self-Organizing Maps
An adaptive rule extraction with the fuzzy self-organizing map and a comparison with other methods
ISUMA '95 Proceedings of the 3rd International Symposium on Uncertainty Modelling and Analysis
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Aggregate distance based clustering using fibonacci series-FIBCLUS
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
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This paper introduces a fuzzy-extension of the Kohonen Self Organizing Map model called Fuzzy Growing Hierarchical SOM that is able to extract Fuzzy rules in hierarchical way. The main idea of the FGHSOM is to provide an architecture that can be initialized with prior knowledge and without, and can be trained directly using SOM learning methods. The training is carried out using competitive methods in such a way that the learning result is interpretable in the form of linguistic fuzzy if-then rules and rules are organized in a tree-like structure. The structure allows increasing the information using parent/child relationships. The FGHSOM is successfully compared with different neuro-fuzzy algorithms in different classification problems.