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

来源：

2009 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING (16TH), VOLS I AND II, CONFERENCE PROCEEDINGS | 2009年

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

页码：176 / 182

页数：7

共 50 条

[21] Analysis of algorithms for radial basis function neural network
Stastny, Jiri
Skorpil, Vladislav
PERSONAL WIRELESS COMMUNICATIONS, 2007, 245 : 54 - +
[22] Coordinate transformation by radial basis function neural network
Gullu, Mevlut
SCIENTIFIC RESEARCH AND ESSAYS, 2010, 5 (20): : 3141 - 3146
[23] A Novel Reformulated Radial Basis Function Neural Network
Yin, Jianchuan
Hu, Jiangqiang
Bu, Renxiang
CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 2997 - 3001
[24] Radial basis function neural network for a traffic model
Luo, Zanwen
Wu, Zhijian
Han, Zengjin
Qinghua Daxue Xuebao/Journal of Tsinghua University, 2001, 41 (09): : 106 - 110
[25] Chaotic Neural Network with Radial Basis Function Disturbance
Xu, Nan
Xu, Yao-Qun
ELEVENTH WUHAN INTERNATIONAL CONFERENCE ON E-BUSINESS, 2012, : 668 - +
[26] Research on motion compensation method based on neural network of radial basis function
Zuo Yunbo
仪器仪表学报, 2014, 35(S2) (S2) : 215 - 218
[27] Research on Intention Recognition Method Based on Radial Basis Function Neural Network
Yan, Han
Ming, Han
Yang, Ruoxi
Li, Tiejun
INFORMATION TECHNOLOGY AND CONTROL, 2019, 48 (04): : 637 - 647
[28] An obsolescence forecasting method based on improved radial basis function neural network
Liu, Yan
Zhao, Min
AIN SHAMS ENGINEERING JOURNAL, 2022, 13 (06)
[29] Beam Pattern Optimization Method Based on Radial Basis Function Neural Network
Ren Xiaoying
Wang Yingmin
Wang Qi
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2021, 43 (12) : 3695 - 3702
[30] Digital Design of Radial Basis Function Neural Network and Recurrent Neural Network
Sahithya, P.
Arulmozhi, M.
Praveen, Nandini
2019 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET 2019): ADVANCING WIRELESS AND MOBILE COMMUNICATIONS TECHNOLOGIES FOR 2020 INFORMATION SOCIETY, 2019, : 393 - 397

← 1 2 3 4 5 →