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Abstract
A fuzzy rule-based evidential reasoning approach and it corresponding optimization algorithm have been proposed recently, where a fuzzy rule-base with a belief structure, called a fuzzy belief rule base (FBRB), forms a basis in the inference mechanism. In this paper, a new learning method for optimally generating a consistent FBRB based on the given data is proposed. The main focus is given on the consistency of FBRB knowing that the consistency conditions are often violated if the system is generated from real world data. The measurement of inconsistency of FBRB is provided and finally is incorporated in the objective function of the optimization algorithm. This process is formulated as a nonlinear constraint optimization problem and solved using the optimization tool provided in MATLAB. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm.
Keywords
SETS, SYSTEMS, METHODOLOGY, INFERENCE

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Chicago
Liu, Jun, Luis Martínez, Da Ruan, and Hui Wang. 2010. “Generating Consistent Fuzzy Belief Rule Base from Sample Data.” In World Scientific Proceedings Series on Computer Engineering and Information Science, ed. Koen Vanhoof, Da Ruan, Tianrui Li, and Geert Wets, 2:167–172. Athens, Greece: World Scientific and Engineering Academy and Society.
APA
Liu, Jun, Martínez, L., Ruan, D., & Wang, H. (2010). Generating consistent fuzzy belief rule base from sample data. In K. Vanhoof, D. Ruan, T. Li, & G. Wets (Eds.), World Scientific Proceedings Series on Computer Engineering and Information Science (Vol. 2, pp. 167–172). Presented at the 4th International conference on Intelligent Systems and Knowledge Engineering (ISKE 2009), Athens, Greece: World Scientific and Engineering Academy and Society.
Vancouver
1.
Liu J, Martínez L, Ruan D, Wang H. Generating consistent fuzzy belief rule base from sample data. In: Vanhoof K, Ruan D, Li T, Wets G, editors. World Scientific Proceedings Series on Computer Engineering and Information Science. Athens, Greece: World Scientific and Engineering Academy and Society; 2010. p. 167–72.
MLA
Liu, Jun, Luis Martínez, Da Ruan, et al. “Generating Consistent Fuzzy Belief Rule Base from Sample Data.” World Scientific Proceedings Series on Computer Engineering and Information Science. Ed. Koen Vanhoof et al. Vol. 2. Athens, Greece: World Scientific and Engineering Academy and Society, 2010. 167–172. Print.
@inproceedings{2918707,
  abstract     = {A fuzzy rule-based evidential reasoning approach and it corresponding optimization algorithm have been proposed recently, where a fuzzy rule-base with a belief structure, called a fuzzy belief rule base (FBRB), forms a basis in the inference mechanism. In this paper, a new learning method for optimally generating a consistent FBRB based on the given data is proposed. The main focus is given on the consistency of FBRB knowing that the consistency conditions are often violated if the system is generated from real world data. The measurement of inconsistency of FBRB is provided and finally is incorporated in the objective function of the optimization algorithm. This process is formulated as a nonlinear constraint optimization problem and solved using the optimization tool provided in MATLAB. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm.},
  author       = {Liu, Jun and Mart{\'i}nez, Luis and Ruan, Da and Wang, Hui},
  booktitle    = {World Scientific Proceedings Series on Computer Engineering and Information Science},
  editor       = {Vanhoof, Koen and Ruan, Da and Li, Tianrui and Wets, Geert},
  isbn         = {9789814295055},
  keyword      = {SETS,SYSTEMS,METHODOLOGY,INFERENCE},
  language     = {eng},
  location     = {Hasselt, Belgium},
  pages        = {167--172},
  publisher    = {World Scientific and Engineering Academy and Society},
  title        = {Generating consistent fuzzy belief rule base from sample data},
  url          = {http://dx.doi.org/10.1142/9789814295062\_0026},
  volume       = {2},
  year         = {2010},
}

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