NDLI: A new annealing robust fuzzy basis function for modeling with outliers

Content Provider	IEEE Xplore Digital Library
Author	Chen-Chia Chuang Yung-Cheng Lee Jin-Tsong Jeng
Copyright Year	2003
Description	Author affiliation: Dept. of Inf. & Electron. Commerce, Kai Nan Univ., Taoyuan, Taiwan (Chen-Chia Chuang)
Abstract	In this paper, the annealing robust fuzzy basis function (ARFBF) is proposed to improve the problems of the fuzzy basis function for modeling with outliers. Firstly, the repeated support vector regression (RSVR) approach is proposed to determine the initial structure of ARFBF in this paper. Because of a RSVR approach is equivalent to solving twice linear constrained quadratic programming problem under a fixed structure of SVR, the number of hidden nodes, initial parameters and initial weights of the ARFBF are easily obtained in the second SVR. Secondly, the results of RSVR are used as initial structure in ARFBF. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for ARFBF. That is, an ARLA is proposed to overcome the problems of initialization and the cut-off points in the robust learning algorithm. Hence, when an initial structure of ARFBF is determined by a RSVR approach, the ARFBF with ARLA have fast convergence speed and robust against outliers. Simulation results are provided to show the validity and applicability of the proposed ARFBF.
Sponsorship	Syst., Man & Cybernetics Soc. IEEE
Starting Page	4451
Ending Page	4456
File Size	368889
Page Count	6
File Format	PDF
ISBN	0780379527
ISSN	1062922X
DOI	10.1109/ICSMC.2003.1245685
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-10-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Vectors Quadratic programming Convergence Simulated annealing Radial basis function networks Electronic commerce Computer science Data engineering Electronic mail
Content Type	Text
Resource Type	Article

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