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

http://doi.org/10.1631/jzus.C1300370


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.

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author="Zhi-qiang Feng, Cun-gen Liu, Hu Huang",
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DOI - 10.1631/jzus.C1300370


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.

基于区间值模糊粗糙集的知识建模及相似性推理:焊接变形预报

研究目的:知识获取和知识推理是智能系统开发中的两大环节。基于知识的非机理性建模方法已成为复杂过程建模的一种趋势。为解决建模过程中对经验知识的依赖问题,进一步完善推理机制,本文基于粗糙集和区间值模糊集理论,研究知识建模及近似推理方法,并将其应用于船体结构焊接变形预报。对建模与推理中的理论、方法和实际问题的研究有助于认识焊接变形规律,并可进一步推广至其他复杂过程,促进系统建模理论的发展。
创新要点:将区间值模糊集与粗糙集理论结合,通过引入新的包含度来构造区间值模糊粗糙集模型,经过数据采集、区间值模糊化、属性约简、规则抽取等步骤,从信息系统中提取出一个简化的模糊知识模型,给出获取模糊知识模型的完整算法;通过对经典的合成规则推理与现有的相似性推理的机理分析,提出一种新的相似性推理--基于合成规则的相似性推理方法。
方法提亮:与现有的智能方法相比,本文的知识建模方法不依赖于经验知识,所构建的模型易于理解和编辑,运行速度快,计算精度较高,对复杂过程建模有较强的适应性。改进的相似性推理方法,既考虑规则前提与结论之间的内在关联,又把相似性匹配作为必要环节,这样,输入和前提所发生的变化均能在输出中反映出来,推理结果更趋合理。
重要结论:将上述方法应用在焊接变形预报方面,实验结果验证了算法有效性,表明算法对复杂过程建模具有较强适应性。
知识建模;区间值模糊粗糙集;相似性推理;焊接变形预报

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