Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28226
Title: Knowledge-leverage-based fuzzy system and its modeling
Authors: Deng, Z
Jiang, Y
Chung, FL 
Ishibuchi, H
Wang, S
Keywords: Fuzzy modeling
Fuzzy systems
Knowledge leverage
Mamdani-Larsen fuzzy model
Missing data
Reduced set density estimator (RSDE)
Transfer learning
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on fuzzy systems, 2013, v. 21, no. 4, 6263294, p. 597-609 How to cite?
Journal: IEEE transactions on fuzzy systems 
Abstract: The classical fuzzy system modeling methods only consider the current scene where the training data are assumed fully collectable. However, if the available data from that scene are insufficient, the fuzzy systems trained will suffer from weak generalization for the modeling task in this scene. In order to overcome this problem, a fuzzy system with knowledge-leverage capability, which is known as a knowledge-leverage-based fuzzy system (KL-FS), is proposed in this paper. The KL-FS not only makes full use of the data from the current scene in the learning procedure but can effectively make leverage on the existing knowledge from the reference scene, e.g., the parameters of a fuzzy system obtained from a reference scene, as well. Specifically, a knowledge-leverage- based Mamdani-Larsen-type fuzzy system (KL-ML-FS) is proposed by using the reduced set density estimation technique integrating with the corresponding knowledge-leverage mechanism. The new fuzzy system modeling technique has been verified by experiments on synthetic and real-world datasets, where KL-ML-FS has better performance and adaptability than the traditional fuzzy modeling methods in scenarios with insufficient data.
URI: http://hdl.handle.net/10397/28226
ISSN: 1063-6706
EISSN: 1941-0034
DOI: 10.1109/TFUZZ.2012.2212444
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

26
Last Week
1
Last month
1
Citations as of Dec 15, 2017

WEB OF SCIENCETM
Citations

24
Last Week
0
Last month
0
Citations as of Dec 8, 2017

Page view(s)

85
Last Week
3
Last month
Checked on Dec 11, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.