Robust regression and outlier detection
Robust regression and outlier detection
The nature of statistical learning theory
The nature of statistical learning theory
Fuzzy engineering
A course in fuzzy systems and control
A course in fuzzy systems and control
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Robust clustering methods: a unified view
IEEE Transactions on Fuzzy Systems
Fuzzy basis functions: comparisons with other basis functions
IEEE Transactions on Fuzzy Systems
Robust error measure for supervised neural network learning with outliers
IEEE Transactions on Neural Networks
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
Outliers in biometrical data: What's old, What's new
International Journal of Biometrics
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In this paper, an annealing robust fuzzy basis function (ARFBF) is proposed to improve the problems of the fuzzy basis function (FBF) for modelling with noise and outliers. Firstly, the repeated support vector regression (RSVR) approach is proposed to determine the initial structure of ARFBF. Because a RSVR approach is equivalent to solving twice linear constrained quadratic programming problem under a fixed structure of SVR, the number of hidden nodes, the initial parameters and the initial weights of ARFBF are easily obtained in the RSVR. Secondly, the results of RSVR are used as initial structure in the ARFBF. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for ARFBF, and applied to adjust the parameters as well as weights of ARFBF. That is, an ARLA is proposed to overcome the problems of initialisation and the cutoff points in the robust learning algorithm. Hence, when an initial structure of ARFBF is determined by a RSVR approach, the ARFBF with ARLA has faster convergence speed and is robust against outliers. Simulation results are provided to show the validity and applicability of the proposed ARFBF.