Please wait a minute...
Front. Inform. Technol. Electron. Eng.  2014, Vol. 15 Issue (8): 636-650    DOI: 10.1631/jzus.C1300370
    
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
Zhi-qiang Feng, Cun-gen Liu, Hu Huang
Maritime College, Qinzhou University, Qinzhou 535000, China; State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion
Zhi-qiang Feng, Cun-gen Liu, Hu Huang
Maritime College, Qinzhou University, Qinzhou 535000, China; State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
 全文: PDF 
摘要: 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。
关键词: 知识建模区间值模糊粗糙集相似性推理焊接变形预报    
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.
Key words: Knowledge modeling    Interval-valued fuzzy rough set    Similarity-based inference    Welding distortion prediction
收稿日期: 2013-12-18 出版日期: 2014-08-06
CLC:  TP18  
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
Zhi-qiang Feng
Cun-gen Liu
Hu Huang

引用本文:

Zhi-qiang Feng, Cun-gen Liu, Hu Huang. Knowledge modeling based on interval-valued fuzzy rough set and similarity inference: prediction of welding distortion. Front. Inform. Technol. Electron. Eng., 2014, 15(8): 636-650.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1300370        http://www.zjujournals.com/xueshu/fitee/CN/Y2014/V15/I8/636

[1] Muhammad Asif Zahoor Raja, Iftikhar Ahmad, Imtiaz Khan, Muhammed Ibrahem Syam, Abdul Majid Wazwaz. 用于解决非线性受电弓系统的启发式神经网络计算[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 464-484.
[2] Nan-ning Zheng, Zi-yi Liu, Peng-ju Ren, Yong-qiang Ma, Shi-tao Chen, Si-yu Yu, Jian-ru Xue, Ba-dong Chen, Fei-yue Wang. 混合-增强智能:协作与认知[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 153-179.
[3] Jian-ru Xue, Di Wang, Shao-yi Du, Di-xiao Cui, Yong Huang, Nan-ning Zheng. 无人车自主定位和障碍物感知的视觉主导多传感器融合方法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 122-138.
[4] Bo-hu Li, Bao-cun Hou, Wen-tao Yu, Xiao-bing Lu, Chun-wei Yang. 人工智能在智能制造领域的应用研究[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 86-96.
[5] Hao Fang, Shao-lei Lu, Jie Chen, Wen-jie Chen. 基于面向任务的协同特征向量的联盟形成算法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 139-148.
[6] Wei Li, Wen-jun Wu, Huai-min Wang, Xue-qi Cheng, Hua-jun Chen, Zhi-hua Zhou, Rong Ding. AI2.0时代的群体智能[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 15-43.
[7] Tao-cheng Hu, Jin-hui Yu. 基于最大间隔的贝叶斯分类器[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(10): 973-981.
[8] Jian-hua Dai, Hu Hu, Guo-jie Zheng, Qing-hua Hu, Hui-feng Han, Hong Shi. 区间值信息系统中基于信息熵的属性约简[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(9): 919-928.
[9] Izabela Nielsen, Robert Wójcik, Grzegorz Bocewicz, Zbigniew Banaszak. 模糊操作时间约束下的多模过程优化:声明式建模方法[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(4): 338-347.
[10] Maiquel de Brito, Lauren Thévin, Catherine Garbay, Olivier Boissier, Jomi Fred Hübner. 通过情景化人工机构(SAI)实现灵活的危机管理校准[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(4): 309-324.
[11] Jo?o Carneiro, Diogo Martinho, Goreti Marreiros, Paulo Novais. 应用于普适群体决策的智能谈判模型[J]. Front. Inform. Technol. Electron. Eng., 2016, 17(4): 296-308.
[12] Yu-qing Chen, Yu-pu Diao, Jing-gang Duan, Li-yuan Cui, Jia-yi Zhang. 后胚胎期小鼠外膝体和上丘双眼分离的发育[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(10): 807-812.
[13] Ya-tao Zhang, Cheng-yu Liu, Shou-shui Wei, Chang-zhi Wei, Fei-fei Liu. 基于非线性支持向量机和遗传算法的移动ECG质量评估[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(7): 564-573.
[14] Feng-fei Zhao, Zheng Qin, Zhuo Shao, Jun Fang, Bo-yan Ren. 用于在线值函数近似的贪婪特征替换方法[J]. Front. Inform. Technol. Electron. Eng., 2014, 15(3): 223-231.
[15] Ali Uysal, Raif Bayir. Real-time condition monitoring and fault diagnosis in switched reluctance motors with Kohonen neural network[J]. Front. Inform. Technol. Electron. Eng., 2013, 14(12): 941-952.