Knowledge-leverage based TSK fuzzy system with improved knowledge transfer

Zhaohong Deng; Yizhang Jiang; Longbing Cao; Shitong Wang

doi:10.1109/FUZZ-IEEE.2014.6891544

Knowledge-leverage based TSK fuzzy system with improved knowledge transfer

Zhaohong Deng, Yizhang Jiang, Longbing Cao, Shitong Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

13 Citations (Scopus)

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.

Original language	English
Title of host publication	2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	178-185
Number of pages	8
ISBN (Electronic)	9781479920723, 9781479920730
DOIs	https://doi.org/10.1109/FUZZ-IEEE.2014.6891544
Publication status	Published - 2014
Externally published	Yes
Event	2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Publication series

Name
ISSN (Print)	1098-7584

Conference

Conference	2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
Country/Territory	China
City	Beijing
Period	6/07/14 → 11/07/14

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10.1109/FUZZ-IEEE.2014.6891544

Cite this

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title = "Knowledge-leverage based TSK fuzzy system with improved knowledge transfer",

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.",

author = "Zhaohong Deng and Yizhang Jiang and Longbing Cao and Shitong Wang",

year = "2014",

doi = "10.1109/FUZZ-IEEE.2014.6891544",

language = "English",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

pages = "178--185",

booktitle = "2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)",

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note = "2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

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Deng, Z, Jiang, Y, Cao, L & Wang, S 2014, Knowledge-leverage based TSK fuzzy system with improved knowledge transfer. in 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 178-185, 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/FUZZ-IEEE.2014.6891544

Knowledge-leverage based TSK fuzzy system with improved knowledge transfer. / Deng, Zhaohong; Jiang, Yizhang; Cao, Longbing et al.
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2014. p. 178-185.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Knowledge-leverage based TSK fuzzy system with improved knowledge transfer

AU - Deng, Zhaohong

AU - Jiang, Yizhang

AU - Cao, Longbing

AU - Wang, Shitong

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

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UR - http://purl.org/au-research/grants/arc/DP130102691

UR - http://purl.org/au-research/grants/arc/LP120100566

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DO - 10.1109/FUZZ-IEEE.2014.6891544

M3 - Conference proceeding contribution

SP - 178

EP - 185

BT - 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

Knowledge-leverage based TSK fuzzy system with improved knowledge transfer

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