A PSO-GD-based hybrid algorithm for general fuzzy measure determination

Huan-Yu ZHAO; Xi-Zhao WANG

doi:10.1109/ICMLC.2009.5212478

A PSO-GD-based hybrid algorithm for general fuzzy measure determination

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

Abstract

Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzymeasure. However, there exist some limitations. In this paper, we design a hybrid algorithm called GDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.

Original language	English
Title of host publication	Proceedings of the 2009 International Conference on Machine Learning and Cybernetics
Publisher	IEEE
Pages	553-556
Number of pages	4
ISBN (Print)	9781424437023
DOIs	https://doi.org/10.1109/ICMLC.2009.5212478
Publication status	Published - 2009
Externally published	Yes
Event	2009 International Conference on Machine Learning and Cybernetics - Hebei, China Duration: 12 Jul 2009 → 15 Jul 2009

Publication series

Name	International Conference on Machine Learning and Cybernetics (ICMLC)
Publisher	IEEE
ISSN (Print)	2160-133X
ISSN (Electronic)	2160-1348

Conference

Conference	2009 International Conference on Machine Learning and Cybernetics
Country/Territory	China
City	Hebei
Period	12/07/09 → 15/07/09

Bibliographical note

This research is partially supported by the NSF of Hebei Province (F2008000635, 06213548), by the key project of applied fundamental research of Hebei Province (08963522D), and by the plan of first 100 excellent innovative scientists of Education Department in Hebei Province.

Keywords

Fuzzy integral
Fuzzy measure
Gradient descent algorithm
Particle swarm optimization

Access to Document

10.1109/ICMLC.2009.5212478

Cite this

@inproceedings{17a682b98cb34bd890ffdfd647c1775c,

title = "A PSO-GD-based hybrid algorithm for general fuzzy measure determination",

abstract = "Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzymeasure. However, there exist some limitations. In this paper, we design a hybrid algorithm called GDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.",

keywords = "Fuzzy integral, Fuzzy measure, Gradient descent algorithm, Particle swarm optimization",

author = "Huan-Yu ZHAO and Xi-Zhao WANG",

note = "This research is partially supported by the NSF of Hebei Province (F2008000635, 06213548), by the key project of applied fundamental research of Hebei Province (08963522D), and by the plan of first 100 excellent innovative scientists of Education Department in Hebei Province. ; 2009 International Conference on Machine Learning and Cybernetics ; Conference date: 12-07-2009 Through 15-07-2009",

year = "2009",

doi = "10.1109/ICMLC.2009.5212478",

language = "English",

isbn = "9781424437023",

series = "International Conference on Machine Learning and Cybernetics (ICMLC)",

publisher = "IEEE",

pages = "553--556",

booktitle = "Proceedings of the 2009 International Conference on Machine Learning and Cybernetics",

}

ZHAO, H-Y & WANG, X-Z 2009, A PSO-GD-based hybrid algorithm for general fuzzy measure determination. in Proceedings of the 2009 International Conference on Machine Learning and Cybernetics., 5212478, International Conference on Machine Learning and Cybernetics (ICMLC), IEEE, pp. 553-556, 2009 International Conference on Machine Learning and Cybernetics, Hebei, China, 12/07/09. https://doi.org/10.1109/ICMLC.2009.5212478

A PSO-GD-based hybrid algorithm for general fuzzy measure determination. / ZHAO, Huan-Yu; WANG, Xi-Zhao.
Proceedings of the 2009 International Conference on Machine Learning and Cybernetics. IEEE, 2009. p. 553-556 5212478 (International Conference on Machine Learning and Cybernetics (ICMLC)).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

TY - GEN

T1 - A PSO-GD-based hybrid algorithm for general fuzzy measure determination

AU - ZHAO, Huan-Yu

AU - WANG, Xi-Zhao

N1 - This research is partially supported by the NSF of Hebei Province (F2008000635, 06213548), by the key project of applied fundamental research of Hebei Province (08963522D), and by the plan of first 100 excellent innovative scientists of Education Department in Hebei Province.

PY - 2009

Y1 - 2009

N2 - Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzymeasure. However, there exist some limitations. In this paper, we design a hybrid algorithm called GDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.

AB - Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzymeasure. However, there exist some limitations. In this paper, we design a hybrid algorithm called GDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.

KW - Fuzzy integral

KW - Fuzzy measure

KW - Gradient descent algorithm

KW - Particle swarm optimization

UR - http://www.scopus.com/inward/record.url?scp=70350702399&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2009.5212478

DO - 10.1109/ICMLC.2009.5212478

M3 - Conference paper (refereed)

AN - SCOPUS:70350702399

SN - 9781424437023

T3 - International Conference on Machine Learning and Cybernetics (ICMLC)

SP - 553

EP - 556

BT - Proceedings of the 2009 International Conference on Machine Learning and Cybernetics

PB - IEEE

T2 - 2009 International Conference on Machine Learning and Cybernetics

Y2 - 12 July 2009 through 15 July 2009

ER -

A PSO-GD-based hybrid algorithm for general fuzzy measure determination

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this