DocumentCode
3346677
Title
Notice of Retraction
Fuzzy Bayesian Networks and its application in pressure equipment´s security alerts
Author
Qin Liao ; Zhicong Qiu ; Jiepeng Zeng
Author_Institution
Sch. of Math. Sci., South China Univ. of Technol., Guangzhou, China
Volume
3
fYear
2011
fDate
26-28 July 2011
Firstpage
1507
Lastpage
1511
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Because attribute variables, namely nodes of Bayesian Networks (BN) may have the characteristics of fuzziness and randomness simultaneously, a Fuzzy Bayesian Network (FBN) algorithm is proposed in this paper. We define fuzzy probability and Conditional Fuzzy Probability Table (CFPT) to express the relationship among variables having mixed uncertainty. We use genetic algorithm to optimize structure learning and parameters learning, feedback to find the optimal network structure according to reasoning error, and fix network parameters at the same time by modifying the parameters of membership function. Finally, we use the FBN algorithm to build the fuzzy Bayesian network and to knowledge reasoning on the data of industrial boilers´ security alerts. Results demonstrate that FBN algorithm applied in mixed uncertainty problems is more effective compared with existing BN algorithms.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Because attribute variables, namely nodes of Bayesian Networks (BN) may have the characteristics of fuzziness and randomness simultaneously, a Fuzzy Bayesian Network (FBN) algorithm is proposed in this paper. We define fuzzy probability and Conditional Fuzzy Probability Table (CFPT) to express the relationship among variables having mixed uncertainty. We use genetic algorithm to optimize structure learning and parameters learning, feedback to find the optimal network structure according to reasoning error, and fix network parameters at the same time by modifying the parameters of membership function. Finally, we use the FBN algorithm to build the fuzzy Bayesian network and to knowledge reasoning on the data of industrial boilers´ security alerts. Results demonstrate that FBN algorithm applied in mixed uncertainty problems is more effective compared with existing BN algorithms.
Keywords
Bayes methods; boilers; fuzzy set theory; genetic algorithms; inference mechanisms; knowledge based systems; probability; production engineering computing; production equipment; CFPT; FBN algorithm; conditional fuzzy probability table; fuzzy Bayesian network; genetic algorithm; industrial boiler; knowledge reasoning; membership function; parameter learning; pressure equipment security alert; structure learning; Bayesian methods; Cognition; Fuzzy sets; Genetic algorithms; Genetics; Knowledge engineering; Mathematical model; conditional fuzzy probability table; fuzzy Bayesian networks; genetic algorithm; knowledge reasoning;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022325
Filename
6022325
Link To Document