DocumentCode
226438
Title
Knowledge-leverage based TSK fuzzy system with improved knowledge transfer
Author
Zhaohong Deng ; Yizhang Jiang ; Longbing Cao ; Shitong Wang
Author_Institution
Sch. of Digital Media, Jiangnan Univ., Wuxi, China
fYear
2014
fDate
6-11 July 2014
Firstpage
178
Lastpage
185
Abstract
In this study, the improved knowledge-leverage based TSK fuzzy system modeling method is proposed in order to overcome the weaknesses of the knowledge-leverage based TSK fuzzy system (TSK-FS) modeling method. In particular, two improved knowledge-leverage strategies have been introduced for the parameter learning of the antecedents and consequents of the TSK-FS constructed in the current scene by transfer learning from the reference scene, respectively. With the improved knowledge-leverage learning abilities, the proposed method has shown the more adaptive modeling effect compared with traditional TSK fuzzy modeling methods and some related methods on the synthetic and real world datasets.
Keywords
fuzzy systems; knowledge based systems; learning (artificial intelligence); TSK-FS; adaptive modeling effect; improved knowledge transfer; knowledge-leverage based TSK fuzzy system modeling method; knowledge-leverage learning abilities; transfer learning; Adaptation models; Data models; Educational institutions; Fuzzy systems; Learning systems; Linear programming; Training; Fuzzy modeling; Fuzzy systems; Improved KL-TSK-FS; Knowledge leverage; Missing data; Transfer learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4799-2073-0
Type
conf
DOI
10.1109/FUZZ-IEEE.2014.6891544
Filename
6891544
Link To Document