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- W2473531749 abstract "The neuro-fuzzy modeling has an intrinsic inconsistency. It may perform thinking tolerant to imprecision, but neural networks learning methods are zero-tolerant to imprecision. Proposed method make it possible to exclude this intrinsic inconsistency of neuro-fuzzy modeling. This new method can be called ε-insensitive learning or εlearning, where in order to fit fuzzy model to real data, ε-insensitive loss function is used. Computationally efficient numerical method for the ε-insensitive learning is proposed. Finally, numerical example is given to demonstrate the improved generalization ability of obtained fuzzy model." @default.
- W2473531749 created "2016-07-22" @default.
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- W2473531749 date "2003-01-01" @default.
- W2473531749 modified "2023-10-16" @default.
- W2473531749 title "An ε-insensitive Learning in Neuro-Fuzzy Modeling" @default.
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- W2473531749 doi "https://doi.org/10.1007/978-3-7908-1902-1_81" @default.
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