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 :
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