OPTIMIZATION OF MULTIPLE RESPONSE PROCESS BY NEURAL NETWORKS BASED ON DESIRABILITY CONCEPT

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

BASHIRI MAHDI; HOSSEININEZHAD S.J.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN) Year:2010 | Volume:20 | Issue:4 Page(s): 53-63

Download Full-Text

Persian Verion

View:

1,206

Download:

Cites:

Information Journal Paper

Title

OPTIMIZATION OF MULTIPLE RESPONSE PROCESS BY NEURAL NETWORKS BASED ON DESIRABILITY CONCEPT

Author(s)

BASHIRI MAHDI | HOSSEININEZHAD S.J. | Issue Writer Certificate

Keywords

RESPONSE SURFACE METHODOLOGY (RSM)Q2

MULTIPLE RESPONSE OPTIMIZATION (MRO)Q3

DESIRABILITY FUNCTIONQ2

FEED FOR WARD BACK PROPAGATION NEURAL NETWORKQ3

Abstract

In this paper, a method is proposed for Multiple Response Optimization (MRO) by neural networks and uses desirability of each response for forecasting. The used neural network is a feed forward back propagation one with two hidden layers. The numbers of neurons in the hidden layers are determined using MSE criterion for training and test data. The numbers on neurons of the first layer last layer are equal to the numbers of the factors and responses, respectively. After training the network, forecasting phase are done by giving different factor levels to calculate desirability of different experiments. Then total desirability is calculated. The optimal combination is which have the greatest total desirability. Finally, a numerical example is expressed to illustrate the capability of the neural network. The results of the research shows that although determining of suitable neural network is time consuming but have more accuracy than Response Surface Methodology (RSM). Also, the obtained optimal combination from RSM is one of optimal solution by neural network.

Cites

No record.

References

No record.

Cite

APA: Copy

BASHIRI, MAHDI, & HOSSEININEZHAD, S.J.. (2010). OPTIMIZATION OF MULTIPLE RESPONSE PROCESS BY NEURAL NETWORKS BASED ON DESIRABILITY CONCEPT. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN), 20(4), 53-63. SID. https://sid.ir/paper/65978/en

Vancouver: Copy

BASHIRI MAHDI, HOSSEININEZHAD S.J.. OPTIMIZATION OF MULTIPLE RESPONSE PROCESS BY NEURAL NETWORKS BASED ON DESIRABILITY CONCEPT. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)[Internet]. 2010;20(4):53-63. Available from: https://sid.ir/paper/65978/en

IEEE: Copy

MAHDI BASHIRI, and S.J. HOSSEININEZHAD, “OPTIMIZATION OF MULTIPLE RESPONSE PROCESS BY NEURAL NETWORKS BASED ON DESIRABILITY CONCEPT,” INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN), vol. 20, no. 4, pp. 53–63, 2010, [Online]. Available: https://sid.ir/paper/65978/en

Related Journal Papers