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• W2011190771 abstract "Resistant training in radial basis function (RBF) networks is the topic of this paper. In this paper, one modification of Gauss-Newton training algorithm based on the theory of robust regression for dealing with outliers in the framework of function approximation, system identification and control is proposed. This modification combines the numerical ro- bustness of a particular class of non-quadratic estimators known as M-estimators in Statistics and dead-zone. The al- gorithms is tested on some examples, and the results show that the proposed algorithm not only eliminates the influence of the outliers but has better convergence rate then the standard Gauss-Newton algorithm." @default.
• W2011190771 created "2016-06-24" @default.
• W2011190771 creator A5039201416 @default.
• W2011190771 creator A5084913266 @default.
• W2011190771 date "2011-01-01" @default.
• W2011190771 modified "2023-10-01" @default.
• W2011190771 title "Function Approximation Using Robust Radial Basis Function Networks" @default.
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• W2011190771 doi "https://doi.org/10.4236/jilsa.2011.31003" @default.
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