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R. E. Frank, M. Hampel, M. Rohchetti and W. A. Stanel, “Robust Statistics: The Approach Based on Influence Functions,” John Wiley & Sons Inc., Hoboken, 1986.

has been cited by the following article:

• TITLE: Function Approximation Using Robust Radial Basis Function Networks

  AUTHORS: Oleg Rudenko, Oleksandr Bezsonov

  KEYWORDS: Neural Network, Robust Training, Basis Function, Dead Zone

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.3 No.1, February 24, 2011

  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.