Study on adaptive least trimmed squares fuzzy neural network

Liao, SH; Han, MF; Chang, JY; Lin, CT

Study on adaptive least trimmed squares fuzzy neural network

Liao, SH Han, MF Chang, JY Lin, CT

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Publication Type:: Journal Article
Citation:: International Journal of Fuzzy Systems, 2013, 15 (3), pp. 338 - 346
Issue Date:: 2013-09-01

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Field	Value	Language
dc.contributor.author	Liao, SH	en_US
dc.contributor.author	Han, MF	en_US
dc.contributor.author	Chang, JY	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.issued	2013-09-01	en_US
dc.identifier.citation	International Journal of Fuzzy Systems, 2013, 15 (3), pp. 338 - 346	en_US
dc.identifier.issn	1562-2479	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116954
dc.description.abstract	In the largest samplings of data, outliers are observations that are well separated from the major samples. To deal with outlier problems, a least trimmed squares (LTS) estimator is developed for robust linear regression problems. It is meaningful to generalize the LTS estimator to fuzzy neural network (FNN) for robust nonlinear regression problems. In addition, the determination of the trimming constant is important when using the LTS estimator. In this paper, we propose the use of an adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies a scale estimate to a LTS-FNN. This paper particularly emphasizes the robustness of the proposed network against outliers and an automatic determination of the trimming percentage. Simulation problems are provided to compare the performance of the proposed ALTS-FNN, with an LTS-FNN and typical FNN. Simulation results show that the proposed ALTS-FNN is highly robust against outliers. © 2013 TFSA.	en_US
dc.relation.ispartof	International Journal of Fuzzy Systems	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Study on adaptive least trimmed squares fuzzy neural network	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	15	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	15	en_US

Abstract:

In the largest samplings of data, outliers are observations that are well separated from the major samples. To deal with outlier problems, a least trimmed squares (LTS) estimator is developed for robust linear regression problems. It is meaningful to generalize the LTS estimator to fuzzy neural network (FNN) for robust nonlinear regression problems. In addition, the determination of the trimming constant is important when using the LTS estimator. In this paper, we propose the use of an adaptive least trimmed squares fuzzy neural network (ALTS-FNN), which applies a scale estimate to a LTS-FNN. This paper particularly emphasizes the robustness of the proposed network against outliers and an automatic determination of the trimming percentage. Simulation problems are provided to compare the performance of the proposed ALTS-FNN, with an LTS-FNN and typical FNN. Simulation results show that the proposed ALTS-FNN is highly robust against outliers. © 2013 TFSA.

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http://hdl.handle.net/10453/116954