Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design
Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvemen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/15/6768 |
| _version_ | 1797525878535618560 |
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| author | Tuan-Ho Le Hyeonae Jang Sangmun Shin |
| author_facet | Tuan-Ho Le Hyeonae Jang Sangmun Shin |
| author_sort | Tuan-Ho Le |
| collection | DOAJ |
| description | Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method. |
| first_indexed | 2024-03-10T09:20:16Z |
| format | Article |
| id | doaj.art-d665ae001be3491b9585a6ac49d04fe5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:20:16Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d665ae001be3491b9585a6ac49d04fe52023-11-22T05:19:20ZengMDPI AGApplied Sciences2076-34172021-07-011115676810.3390/app11156768Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust DesignTuan-Ho Le0Hyeonae Jang1Sangmun Shin2Department of Electrical Engineering, Faculty of Engineering and Technology, Quy Nhon University, Binh Dinh 591417, VietnamDepartment of Technology Management Engineering, Jeonju University, Jeonju 55069, KoreaDepartment of Industrial & Management Systems Engineering, Dong-A University, Busan 49315, KoreaResponse surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.https://www.mdpi.com/2076-3417/11/15/6768response surface methodologyneural networkdesirability functionmaximum-likelihood estimationrobust design |
| spellingShingle | Tuan-Ho Le Hyeonae Jang Sangmun Shin Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design Applied Sciences response surface methodology neural network desirability function maximum-likelihood estimation robust design |
| title | Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design |
| title_full | Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design |
| title_fullStr | Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design |
| title_full_unstemmed | Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design |
| title_short | Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design |
| title_sort | determination of the optimal neural network transfer function for response surface methodology and robust design |
| topic | response surface methodology neural network desirability function maximum-likelihood estimation robust design |
| url | https://www.mdpi.com/2076-3417/11/15/6768 |
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