Radial Basis Function Neural Network Method of Determining Functional Relationships for Quality Function Deployment

被引:0
|
作者
Li Xin [1 ]
Huang Lu-cheng [1 ]
机构
[1] Beijing Univ Technol, Sch Econ & Management, Beijing 100124, Peoples R China
关键词
functional relationships; house of quality; quality function deployment; radial basis function; ENGINEERING CHARACTERISTICS; PRODUCT DEVELOPMENT; QFD;
D O I
10.1109/ICMSE.2009.5317507
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Quality Function Deployment (QFD) is a systematic approach that captures customer requirements and translates them, through house of quality (HOQ), into engineering characteristics of product. As the functional relationships between customer requirements and engineering characteristics in QFD are uncertain, unclear and fuzzy, Radial Basis Function (RBF) to determine the functional relationships for QFD is presented, and a QFD functional relationships model based on RBF is proposed. According to RBF neural network can realize the nonlinear mapping space from the input space to the output, and can obtain the optimal relationships pattern of the input and output, the customer requirements and engineering characteristics in QFD constituted the input and output of the RBF Neural Network respectively, the optimal relationships are constructed through the neural network training. A case study of natural lighting products development is provided to illustrate the application of the presented method.
引用
收藏
页码:176 / 182
页数:7
相关论文
共 50 条
  • [1] Rough set method of determining functional relationships for quality function deployment
    Ministry of Ecucation Key Lab. of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China
    不详
    不详
    Jisuanji Jicheng Zhizao Xitong, 2007, 8 (1650-1657):
  • [2] The ridge method in a radial basis function neural network
    Praga-Alejo, Rolando J.
    Gonzalez-Gonzalez, David S.
    Cantu-Sifuentes, Mario
    Torres-Trevino, Luis M.
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2015, 79 (9-12): : 1787 - 1796
  • [3] The ridge method in a radial basis function neural network
    Rolando J. Praga-Alejo
    David S. González-González
    Mario Cantú-Sifuentes
    Luis M. Torres-Treviño
    The International Journal of Advanced Manufacturing Technology, 2015, 79 : 1787 - 1796
  • [4] The ridge method in a radial basis function neural network
    20151100633482
    Praga-Alejo, Rolando J. (rolandopraga@comimsa.com), 1787, Springer London (79): : 9 - 12
  • [5] Radial Basis Function Neural Network
    Matera, F
    SUBSTANCE USE & MISUSE, 1998, 33 (02) : 317 - 334
  • [6] Precision of a radial basis function neural network tracking method
    Hanan, J
    Zhou, HY
    Chao, TH
    OPTICAL PATTERN RECOGNITION XIV, 2003, 5106 : 146 - 153
  • [7] A sigmoidal radial basis function neural network for function approximation
    Tsai, JR
    Chung, PC
    Chang, CI
    ICNN - 1996 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS, VOLS. 1-4, 1996, : 496 - 501
  • [8] Median radial basis function neural network
    Bors, AG
    Pitas, I
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 1996, 7 (06): : 1351 - 1364
  • [9] The Normalized Radial Basis Function neural network
    Heimes, F
    van Heuveln, B
    1998 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5, 1998, : 1609 - 1614
  • [10] Bayesian radial basis function neural network
    Yang, ZR
    INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING IDEAL 2005, PROCEEDINGS, 2005, 3578 : 211 - 219