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An Incremental Learning of Concept Drifts Using Evolving Type-2 Recurrent Fuzzy Neural Networks
2017
IEEE transactions on fuzzy systems
the age of big data and dynamic environments result in the increasing demand of advanced machine learning techniques to deal with concept drifts in large data streams. Evolving Fuzzy Systems (EFS) are one of recent initiatives from the fuzzy system community to resolve the issue. Existing EFSs are not robust against data uncertainty, temporal system dynamics, and the absence of system order, because vast majority of EFSs are designed in the feed-forward type-1 fuzzy network architecture. This
doi:10.1109/tfuzz.2016.2599855
fatcat:6pvyhj22e5dsviht6aznjdqdzq