Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system

Jing Zhang; Qiang Gao; Zhi Gang Huang; Zhaoting Huang

doi:10.1109/ICICISYS.2009.5357809

Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system

Jing Zhang^*
, Qiang Gao
, Zhi Gang Huang
, Zhaoting Huang

^*Corresponding author for this work

School of Electronic and Information Engineering

Beihang University
State of Administration of Radio Film and Television

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

3 Scopus citations

Abstract

The fuzzy rule sets, which have been widely used in avionic fault diagnosis system, have considerable redundancy that leads to time-consuming faults location process. In this paper, to reduce the redundant rules, a Multiple Objective Genetic Algorithm, MOGAII, is used to optimize a fuzzy rule set. The optimization problem with two objectives, the maximization diagnostic capability of the system and the minimization number of rules, is formulated. The simulation results show that MOGAII can substantially improve the efficiency of avionics fault diagnosis system comparing with the plain aggregation algorithm, a conventional optimization method of fuzzy rule sets.

Original language	English
Title of host publication	Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Pages	433-437
Number of pages	5
DOIs	https://doi.org/10.1109/ICICISYS.2009.5357809
State	Published - 2009
Event	2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009 - Shanghai, China Duration: 20 Nov 2009 → 22 Nov 2009

Publication series

Name	Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Volume	1

Conference

Conference	2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009
Country/Territory	China
City	Shanghai
Period	20/11/09 → 22/11/09

Keywords

Avionic fault diagnosis
Fuzzy rule
MOGAII
Multiple objective genetic algorithm
Plain aggregation approach

Access to Document

10.1109/ICICISYS.2009.5357809

Cite this

Zhang, J., Gao, Q., Huang, Z. G., & Huang, Z. (2009). Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system. In Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009 (pp. 433-437). Article 5357809 (Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009; Vol. 1). https://doi.org/10.1109/ICICISYS.2009.5357809

Zhang, Jing ; Gao, Qiang ; Huang, Zhi Gang et al. / Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system. Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009. 2009. pp. 433-437 (Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009).

@inproceedings{51a8f080e4364771bcc84435e2d53ed1,

title = "Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system",

abstract = "The fuzzy rule sets, which have been widely used in avionic fault diagnosis system, have considerable redundancy that leads to time-consuming faults location process. In this paper, to reduce the redundant rules, a Multiple Objective Genetic Algorithm, MOGAII, is used to optimize a fuzzy rule set. The optimization problem with two objectives, the maximization diagnostic capability of the system and the minimization number of rules, is formulated. The simulation results show that MOGAII can substantially improve the efficiency of avionics fault diagnosis system comparing with the plain aggregation algorithm, a conventional optimization method of fuzzy rule sets.",

keywords = "Avionic fault diagnosis, Fuzzy rule, MOGAII, Multiple objective genetic algorithm, Plain aggregation approach",

author = "Jing Zhang and Qiang Gao and Huang, \{Zhi Gang\} and Zhaoting Huang",

year = "2009",

doi = "10.1109/ICICISYS.2009.5357809",

language = "英语",

isbn = "9781424447541",

series = "Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009",

pages = "433--437",

booktitle = "Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009",

note = "2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009 ; Conference date: 20-11-2009 Through 22-11-2009",

}

Zhang, J, Gao, Q, Huang, ZG & Huang, Z 2009, Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system. in Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009., 5357809, Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009, vol. 1, pp. 433-437, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009, Shanghai, China, 20/11/09. https://doi.org/10.1109/ICICISYS.2009.5357809

Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system. / Zhang, Jing; Gao, Qiang; Huang, Zhi Gang et al.
Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009. 2009. p. 433-437 5357809 (Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009; Vol. 1).

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

TY - GEN

T1 - Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system

AU - Zhang, Jing

AU - Gao, Qiang

AU - Huang, Zhi Gang

AU - Huang, Zhaoting

PY - 2009

Y1 - 2009

N2 - The fuzzy rule sets, which have been widely used in avionic fault diagnosis system, have considerable redundancy that leads to time-consuming faults location process. In this paper, to reduce the redundant rules, a Multiple Objective Genetic Algorithm, MOGAII, is used to optimize a fuzzy rule set. The optimization problem with two objectives, the maximization diagnostic capability of the system and the minimization number of rules, is formulated. The simulation results show that MOGAII can substantially improve the efficiency of avionics fault diagnosis system comparing with the plain aggregation algorithm, a conventional optimization method of fuzzy rule sets.

AB - The fuzzy rule sets, which have been widely used in avionic fault diagnosis system, have considerable redundancy that leads to time-consuming faults location process. In this paper, to reduce the redundant rules, a Multiple Objective Genetic Algorithm, MOGAII, is used to optimize a fuzzy rule set. The optimization problem with two objectives, the maximization diagnostic capability of the system and the minimization number of rules, is formulated. The simulation results show that MOGAII can substantially improve the efficiency of avionics fault diagnosis system comparing with the plain aggregation algorithm, a conventional optimization method of fuzzy rule sets.

KW - Avionic fault diagnosis

KW - Fuzzy rule

KW - MOGAII

KW - Multiple objective genetic algorithm

KW - Plain aggregation approach

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DO - 10.1109/ICICISYS.2009.5357809

M3 - 会议稿件

AN - SCOPUS:77949592932

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BT - Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009

T2 - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009

Y2 - 20 November 2009 through 22 November 2009

ER -

Zhang J, Gao Q, Huang ZG, Huang Z. Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system. In Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009. 2009. p. 433-437. 5357809. (Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009). doi: 10.1109/ICICISYS.2009.5357809

Optimization of fuzzy rules by muilti-objective genetic algorithm in avionic fault diagnosis system

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Keywords

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