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- W119734876 abstract "When used for function approximation purposes, neural networks and neuro-fuzzy systems belong to a class of models whose parameters can be separated into linear and nonlinear, according to their influence in the model output. This concept of parameter separability can also be applied when the training problem is formulated as the minimization of the integral of the (functional) squared error, over the input domain. Using this approach, the computation of the derivatives involves terms that are dependent only on the model and the input domain, and terms which are the projection of the target function on the basis functions and on their derivatives with respect to the nonlinear parameters, over the input domain. These later terms can be numerically computed with the data." @default.
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- W119734876 date "2013-01-01" @default.
- W119734876 modified "2023-09-25" @default.
- W119734876 title "Exploiting the Functional Training Approach in Takagi-Sugeno Neuro-fuzzy Systems" @default.
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- W119734876 doi "https://doi.org/10.1007/978-3-642-33941-7_48" @default.
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