NDLI: A soft computing technique for noise data with outliers

Content Provider	IEEE Xplore Digital Library
Author	Chen-Chia Chuang Jin-Tsong Jeng
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Eng., Nat. Huwei Inst. of Technol., Yulin Country, Taiwan (Chen-Chia Chuang)
Abstract	In this paper, a soft computing technique is proposed to select an initial structure of neuro-fuzzy networks (NFNs) for the noise data with outlier regression. That is, the proposed soft computing technique is fusion of neural network, fuzzy logic and support vector regression (SVR). Because the SVR approach is equivalent to solving a linear constrained quadratic programming problem under the fixed structure of SVR, the number of hidden nodes and adjustable parameters are easily obtained. That is, the SVR approach is used to obtain the initial structures of the NFNs in the noise data with outlier. Then, an annealing robust learning algorithm (ARLA) uses as the learning algorithm of NFNs. It is applied to adjust the parameters of promise parts and the fuzzy singleton of consequence parts in the NFNs. At the same time, the proposed method is applied to approximate a nonlinear model with outliers. Simulation results are provided to show the validity and applicability of the proposed soft computing technique.
Sponsorship	IEEE Systems, Man and Cybernetics Soc
Starting Page	1171
Ending Page	1176
File Size	366809
Page Count	6
File Format	PDF
ISBN	0780381939
ISSN	18107869
DOI	10.1109/ICNSC.2004.1297113
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-03-21
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Function approximation Computer networks Neural networks Vectors Noise robustness Uncertainty Space technology Radial basis function networks Noise generators
Content Type	Text
Resource Type	Article

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