Study on semiparametric Wilcoxon fuzzy neural networks

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 neura...

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Bibliographic Details
Published inSoft computing (Berlin, Germany) Vol. 16; no. 1; pp. 11 - 21
Main Authors Wu, Hsu-Kun, Lin, Yih-Lon, Hsieh, Jer-Guang, Jeng, Jyh-Horng
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer-Verlag 2012
Springer Nature B.V
Subjects
Artificial Intelligence
Artificial neural networks
Computational Intelligence
Control
Engineering
Fuzzy logic
Fuzzy sets
Machine learning
Mathematical Logic and Foundations
Mechatronics
Neural networks
Original Paper
Outliers (statistics)
Regression models
Robotics
Smoothness
Statistical analysis
Variables
Semiparametric models
Fuzzy neural network (FNN)
Wilcoxon fuzzy neural network (WFNN)
Nonparametric models
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Summary: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.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-011-0730-3