Integration application of Rough Set Theory and BP Neural Network on the assessment of Fabric Smoothness Grade

被引：0
|
作者：
Chen, Huimin [1 ]
Gu, Hongbo [1 ]
Zhang, Weiyuan [1 ]
Mao, Zhiping [1 ]
机构：
[1] Donghua Univ, Key Lab Ecotext, Minist Educ, Shanghai 201620, Peoples R China
来源：
PROCEEDINGS OF FIRST INTERNATIONAL CONFERENCE OF MODELLING AND SIMULATION, VOL I: MODELLING AND SIMULATION IN SCIENCE AND TECHNOLOGY | 2008年
关键词：
fabric; smoothness grade; objective evaluation; RST; BP ANN;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Cotton is an important fiber for apparel applications. However, cotton fiber has poor resiliency. The fabric smoothness grade is one of the most important performance factors. Rough set theory (RST) and artificial neural network (ANN) have been playing important roles in intelligent computing methods. In this paper, a model that integrates RST and back-propagation (BP) ANN was constructed to predict fabric smoothness grade. The model utilizes two methods' advantages. RST efficiently processed the discretization of seven smoothness characterizations, simplified the network's structure, reduced the network's training epochs, and improved the grading accuracy. The simulation results show that the constructed smoothness grading model is effective, and has high grading accuracy.
引用
收藏
页码：204 / 209
页数：6
相关论文
共 50 条
  • [1] Application of Rough Sets to Evaluate Fabric Smoothness Grade
    陈慧敏
    张渭源
    顾洪波
    [J]. Journal of Donghua University(English Edition), 2009, 26 (04) : 356 - 360
  • [2] Application of rough sets to evaluate fabric smoothness grade
    College of Mechanical Engineering, Donghua University, Shanghai 201620, China
    不详
    [J]. J. Donghua Univ, 2009, 4 (356-360):
  • [3] Research and Application of Neural Network Based on Rough Set Theory
    Bo, Zhang
    [J]. 2018 INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SCIENCE AND APPLICATION TECHNOLOGY, 2019, 1168
  • [4] Integration of rough set and neural network for application of generator fault diagnosis
    Su, WJ
    Su, Y
    Zhao, H
    Zhang, XD
    [J]. ROUGH SETS AND CURRENT TRENDS IN COMPUTING, 2004, 3066 : 549 - 553
  • [5] Application of pattern recognition based on rough set and optimized BP neural network
    Zheng, Lianwei
    Zhang, Xuefeng
    Ma, Qianying
    [J]. 2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 3359 - 3362
  • [6] Application of rough set theory and artificial neural network for load forecasting
    Li, QD
    Chi, ZX
    Shi, WB
    [J]. 2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, : 1148 - 1152
  • [7] Research on Case Retrieval Model Based on Rough Set Theory and BP Neural Network
    Wang, Xiaohui
    [J]. 2009 INTERNATIONAL SYMPOSIUM ON INTELLIGENT UBIQUITOUS COMPUTING AND EDUCATION, 2009, : 117 - 120
  • [8] Operational Data Quality evaluation based on BP Neural Network and Rough Set Theory
    Liu Lijun
    [J]. SECOND INTERNATIONAL CONFERENCE ON PHOTONICS AND OPTICAL ENGINEERING, 2017, 10256
  • [9] Survey for the integration of rough set theory with neural networks
    Zhao, Wei-Dong
    Chen, Guo-Hua
    [J]. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2002, 24 (10):
  • [10] Research on Fusion Algorithm with BP Neural Network and Rough Set
    Li Wei
    He Peng-ju
    Zhang Peng
    Chen Ming
    Jiang Li-jun
    [J]. 2011 6TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2011, : 118 - 123
12345