Research Article | OPEN ACCESS
Research on Evaluation Method of Product Style Semantics Based on Neural Network
1Weiguo Zhao, 2Qiang Li, 1Li Wang and 1Chun Yang
1College of Mechanical Engineering, Inner Mongolia University of
Technology, Hohhot 010051, China
2College of Electromechanical Engineering North China University of
Technology, Beijing 430074, China
Research Journal of Applied Sciences, Engineering and Technology 2013 23:4330-4335
Received: September 28, 2012 | Accepted: December 11, 2012 | Published: December 15, 2013
Abstract
This study sets up the corresponding relation between product modeling elements and style semantics based on neural network. First, establish matrices of office chair modeling elements and style semantics by questionnaire method respectively. Then, build standard Back Propagation Neural Network (BPNN), BPNN on Levenberg-Marquardt (L-M) algorithm, standard Radial Basis Function (RBF) neural network and RBFNN on K-means clustering algorithm by MATLAB software and compare the simulating results on two kinds of BPNN and RBFNN. Finally, choose the RBFNN on K-means clustering algorithm as the best model to guide product modeling design. The effectiveness and applicability of this method are demonstrated by experimental results on the office chair design. It is shown that this method not only improves the efficiency of existing products style semantics judgment but also can be used to evaluate the style semantics of each design candidate.
Keywords:
BP neural network on L-M algorithm, product modeling design, RBF neural network on K-means clustering algorithm, standard BP neural network, standard RBF neural network, style semantics,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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