Generating consistent fuzzy belief rule base from sample data

Jun Liu; Luis Martinez; D Ruan; H Wang

doi:10.1142/9789814295062_0026

Generating consistent fuzzy belief rule base from sample data

Jun Liu, Luis Martinez, D Ruan, H Wang

Research output › peer-review

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.

Original language	English
Title of host publication	Intelligent Decision Making Systems, Vol. 2
Editors	T Li, D Ruan, G Wets, K Vanhoof
Place of Publication	Singapore, Singapore
Pages	167-172
Number of pages	6
DOIs	https://doi.org/10.1142/9789814295062_0026
State	Published - 2010
Event	The 4th Int. ISKE Conf. on Intelligent Decision Making Systems - Hasselt Duration: 27 Nov 2009 → 28 Nov 2009

Publication series

Name	Computer Engineering and Information Science
Number	2

Conference

Conference	The 4th Int. ISKE Conf. on Intelligent Decision Making Systems
Country/Territory	Belgium
City	Hasselt
Period	2009-11-27 → 2009-11-28

Access to Document

10.1142/9789814295062_0026License: Unspecified

Cite this

@inproceedings{8898a09380714ce6bfd16ce3e61911d2,

title = "Generating consistent fuzzy belief rule base from sample data",

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 = "Belief structure, Fuzzy logic, Fuzzy belief rule base, Optimization",

author = "Jun Liu and Luis Martinez and D Ruan and H Wang",

note = "Score = 3; The 4th Int. ISKE Conf. on Intelligent Decision Making Systems ; Conference date: 27-11-2009 Through 28-11-2009",

year = "2010",

doi = "10.1142/9789814295062_0026",

language = "English",

isbn = "978-981-4295-05-5",

series = "Computer Engineering and Information Science",

number = "2",

pages = "167--172",

editor = "T Li and D Ruan and G Wets and K Vanhoof",

booktitle = "Intelligent Decision Making Systems, Vol. 2",

}

Liu, J, Martinez, L, Ruan, D & Wang, H 2010, Generating consistent fuzzy belief rule base from sample data. in T Li, D Ruan, G Wets & K Vanhoof (eds), Intelligent Decision Making Systems, Vol. 2. Computer Engineering and Information Science, no. 2, Singapore, Singapore, pp. 167-172, The 4th Int. ISKE Conf. on Intelligent Decision Making Systems, Hasselt, Belgium, 2009-11-27. https://doi.org/10.1142/9789814295062_0026

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AU - Martinez, Luis

AU - Ruan, D

AU - Wang, H

N1 - Score = 3

PY - 2010

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N2 - 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.

AB - 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.

KW - Belief structure

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