Paper Title :A New Robust Design Modeling Method Based On Generalized Regression Neural Network
Author :Tuan-Ho Le, Sangmunshin, Hwa-Il Kim, Sittichai Kaewkuekool
Article Citation :Tuan-Ho Le ,Sangmunshin ,Hwa-Il Kim ,Sittichai Kaewkuekool ,
(2015 ) " A New Robust Design Modeling Method Based On Generalized Regression Neural Network " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 19-23,
Volume-3, Issue-4
Abstract : Over the recent two decades, robust design has been popularly utilized as a powerful method to improve the quality
of products in the offline stage of manufacturing processes. Most estimation methods to define the functional relationships
between the input factors and output responses are based on the response surface methodology (RSM) which requires several
assumptions. Unfortunately, these assumptions are not always hold in the practical industrial problems. Based on the nature of
artificial neural networks (ANNs), these relationships can be conducted without any assumption. Therefore, the
primarymotivation of this paper is to propose the ANNs as an alternative modeling method in robust design. First of all, an
ANN framework-based robust design modeling method is proposed. Secondly, the generalized regression neural
network-based modeling method is proposed to estimatethefunctions between the input and output variables.Finally, a
comparative study between the proposed neural network-based estimation method and the conventional least squares method
based on RSM is conducted in the numerical example. The final results show the efficiency of the proposed neural
network-based modeling method in robust design.
Keywords- Robust Design, Response Surface Methodology, Generalized Regressionneural Network, Estimation
Type : Research paper
Published : Volume-3, Issue-4
DOIONLINE NO - IJACEN-IRAJ-DOIONLINE-1875
View Here
Copyright: © Institute of Research and Journals
|
|
| |
|
PDF |
| |
Viewed - 92 |
| |
Published on 2015-04-13 |
|