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A TSK-Type Neurofuzzy Network Approach to System Modeling Problems
2005
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
We develop a neurofuzzy network technique to extract TSK-type fuzzy rules from a given set of input-output data for system modeling problems. Fuzzy clusters are generated incrementally from the training dataset, and similar clusters are merged dynamically together through input-similarity, output-similarity, and output-variance tests. The associated membership functions are defined with statistical means and deviations. Each cluster corresponds to a fuzzy IF-THEN rule, and the obtained rules
doi:10.1109/tsmcb.2005.846000
pmid:16128458
fatcat:pqbaamdst5f47pjjyr5nlgq2qy