Function Approximation Using Robust Radial Basis Function Networks

• 作者: Oleg Rudenko Oleksandr Bezsonov
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 工学
• 关键词: NEURAL Network ROBUST TRAINING BASIS Function DEAD ZONE
• 年，卷（期）: 2011,（1）
• 页码范围: 17-25
• 页数: 9页
• 分类号: TP1

摘要

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.