International Journal of Advance Computational Engineering and Networking (IJACEN)
.
Follow Us On :
current issues
Volume-10,Issue-4  ( Apr, 2022 )
Past issues
  1. Volume-10,Issue-3  ( Mar, 2022 )
  2. Volume-10,Issue-2  ( Feb, 2022 )
  3. Volume-10,Issue-1  ( Jan, 2022 )
  4. Volume-9,Issue-12  ( Dec, 2021 )
  5. Volume-9,Issue-11  ( Nov, 2021 )
  6. Volume-9,Issue-10  ( Oct, 2021 )
  7. Volume-9,Issue-9  ( Sep, 2021 )
  8. Volume-9,Issue-8  ( Aug, 2021 )
  9. Volume-9,Issue-7  ( Jul, 2021 )
  10. Volume-9,Issue-6  ( Jun, 2021 )

Statistics report
Jul. 2022
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 112
Paper Published : 1361
No. of Authors : 3438
  Journal Paper


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 - 87
| Published on 2015-04-13
   
   
IRAJ Other Journals
IJACEN updates
Paper Submission is open now for upcoming Issue.
The Conference World

JOURNAL SUPPORTED BY