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CLC number: TP18

On-line Access: 2014-08-06

Received: 2013-12-18

Revision Accepted: 2014-05-13

Crosschecked: 2014-07-16

Cited: 1

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Journal of Zhejiang University SCIENCE C 2014 Vol.15 No.8 P.636-650


Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion

Author(s):  Zhi-qiang Feng, Cun-gen Liu, Hu Huang

Affiliation(s):  Maritime College, Qinzhou University, Qinzhou 535000, China; more

Corresponding email(s):   fzqsjtu@163.com, mrhuanghu@126.com

Key Words:  Knowledge modeling, Interval-valued fuzzy rough set, Similarity-based inference, Welding distortion prediction

Zhi-qiang Feng, Cun-gen Liu, Hu Huang. Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion[J]. Journal of Zhejiang University Science C, 2014, 15(8): 636-650.

@article{title="Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion",
author="Zhi-qiang Feng, Cun-gen Liu, Hu Huang",
journal="Journal of Zhejiang University Science C",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion
%A Zhi-qiang Feng
%A Cun-gen Liu
%A Hu Huang
%J Journal of Zhejiang University SCIENCE C
%V 15
%N 8
%P 636-650
%@ 1869-1951
%D 2014
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1300370

T1 - Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion
A1 - Zhi-qiang Feng
A1 - Cun-gen Liu
A1 - Hu Huang
J0 - Journal of Zhejiang University Science C
VL - 15
IS - 8
SP - 636
EP - 650
%@ 1869-1951
Y1 - 2014
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1300370

Knowledge-based modeling is a trend in complex system modeling technology. To extract the process knowledge from an information system, an approach of knowledge modeling based on interval-valued fuzzy rough set is presented in this paper, in which attribute reduction is a key to obtain the simplified knowledge model. Through defining dependency and inclusion functions, algorithms for attribute reduction and rule extraction are obtained. The approximation inference plays an important role in the development of the fuzzy system. To improve the inference mechanism, we provide a method of similarity-based inference in an interval-valued fuzzy environment. Combining the conventional compositional rule of inference with similarity based approximate reasoning, an inference result is deduced via rule translation, similarity matching, relation modification, and projection operation. This approach is applied to the problem of predicting welding distortion in marine structures, and the experimental results validate the effectiveness of the proposed methods of knowledge modeling and similarity-based inference.



Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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