We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Study on semiparametric Wilcoxon fuzzy neural networks.
- Authors
Wu, Hsu-Kun; Lin, Yih-Lon; Hsieh, Jer-Guang; Jeng, Jyh-Horng
- Abstract
Fuzzy neural network (FNN) has long been recognized as an efficient and powerful learning machine for general machine learning problems. Recently, Wilcoxon fuzzy neural network (WFNN), which generalizes the rank-based Wilcoxon approach for linear parametric regression problems to nonparametric neural network, was proposed aiming at improving robustness against outliers. FNN and WFNN are nonparametric models in the sense that they put no restrictions, except possibly smoothness, on the functional form of the regression function. However, they may be difficult to interpret and, even worse, yield poor estimates with high computational cost when the number of predictor variables is large. To overcome this drawback, semiparametric models have been proposed in statistical regression theory. A semiparametric model keeps the easy interpretability of its parametric part and retains the flexibility of its nonparametric part. Based on this, semiparametric FNN and semiparametric WFNN will be proposed in this paper. The learning rules are based on the backfitting procedure frequently used in semiparametric regression. Simulation results show that the semiparametric models perform better than their nonparametric counterparts.
- Subjects
FUZZY neural networks; MACHINE learning; REGRESSION analysis; ALGORITHMS; ARTIFICIAL neural networks
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2012, Vol 16, Issue 1, p11
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-011-0730-3