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

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Abstract

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.

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Authors and Affiliations

  1. Maritime College, Qinzhou University, Qinzhou, 535000, China

    Zhi-qiang Feng & Hu Huang

  2. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Zhi-qiang Feng & Cun-gen Liu

Authors
  1. Zhi-qiang Feng
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  2. Cun-gen Liu
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  3. Hu Huang
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Correspondence to Hu Huang.

Additional information

Project supported by 2013 Comprehensive Reform Pilot of Marine Engineering Specialty (No. ZG0434)

Electronic supplementary materials: The online version of this article (http://dx.dor.org/10.1631/jzus.C1300370) contains supplementary materials, which are available to authorized users

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Feng, Zq., Liu, Cg. & Huang, H. Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion. J. Zhejiang Univ. - Sci. C 15, 636–650 (2014). https://doi.org/10.1631/jzus.C1300370

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  • DOI: https://doi.org/10.1631/jzus.C1300370

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