APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKS
The general goal of this paper is to determine natural frequency of a plate by artificial neural network with various supporting conditions. One of the most famous training of neural network is back propagation algorithm. This algorithm is a systematic method for training multi-layer artificial neur...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
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Semnan University
2014-02-01
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| Series: | مجله مدل سازی در مهندسی |
| Subjects: | |
| Online Access: | https://modelling.semnan.ac.ir/article_1658_da59110e7e04fb41acaa7cc247faee84.pdf |
| _version_ | 1797296695891984384 |
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| author | Ali Heidari Davoud Tavakoli Pouyan Fakharian |
| author_facet | Ali Heidari Davoud Tavakoli Pouyan Fakharian |
| author_sort | Ali Heidari |
| collection | DOAJ |
| description | The general goal of this paper is to determine natural frequency of a plate by artificial neural network with various supporting conditions. One of the most famous training of neural network is back propagation algorithm. This algorithm is a systematic method for training multi-layer artificial neural network. Back propagation algorithm is based on gradient descant which means that it moves downward on the error declination and regulates the weights for the minimum error. In this research, the real frequency is calculated using ANSYS program and is defined as a goal function for neural network so that all outputs of the network can be compared to this function and the error can be calculated. Then using a set of inputs including dimensions or specifications of plate, a neural network is made. After the determination of algorithm and quantities of the network, the phases of training and testing of the results are carried out and the output of the network is created. It is concluded that the results show the performance of the neural network and that the time of frequency calculation is considerably reduced. |
| first_indexed | 2024-03-07T22:08:29Z |
| format | Article |
| id | doaj.art-c5a3df39fb29444789c075e3f4723518 |
| institution | Directory Open Access Journal |
| issn | 2008-4854 2783-2538 |
| language | fas |
| last_indexed | 2024-03-07T22:08:29Z |
| publishDate | 2014-02-01 |
| publisher | Semnan University |
| record_format | Article |
| series | مجله مدل سازی در مهندسی |
| spelling | doaj.art-c5a3df39fb29444789c075e3f47235182024-02-23T18:58:22ZfasSemnan Universityمجله مدل سازی در مهندسی2008-48542783-25382014-02-011135496210.22075/jme.2017.16581658APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKSAli Heidari0Davoud Tavakoli1Pouyan Fakharian2Department of Civil Engineering, University of Shahrekord, Shahrekord, IranPh.D. Student, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Lavizan, IranM.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, IranThe general goal of this paper is to determine natural frequency of a plate by artificial neural network with various supporting conditions. One of the most famous training of neural network is back propagation algorithm. This algorithm is a systematic method for training multi-layer artificial neural network. Back propagation algorithm is based on gradient descant which means that it moves downward on the error declination and regulates the weights for the minimum error. In this research, the real frequency is calculated using ANSYS program and is defined as a goal function for neural network so that all outputs of the network can be compared to this function and the error can be calculated. Then using a set of inputs including dimensions or specifications of plate, a neural network is made. After the determination of algorithm and quantities of the network, the phases of training and testing of the results are carried out and the output of the network is created. It is concluded that the results show the performance of the neural network and that the time of frequency calculation is considerably reduced.https://modelling.semnan.ac.ir/article_1658_da59110e7e04fb41acaa7cc247faee84.pdfeigen problemeigen valueartificial neural networkback propagation |
| spellingShingle | Ali Heidari Davoud Tavakoli Pouyan Fakharian APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKS مجله مدل سازی در مهندسی eigen problem eigen value artificial neural network back propagation |
| title | APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKS |
| title_full | APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKS |
| title_fullStr | APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKS |
| title_full_unstemmed | APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKS |
| title_short | APPROXIMATE EIGENVALUE OF PLATE BY ARTIFICIAL NEURAL NETWORKS |
| title_sort | approximate eigenvalue of plate by artificial neural networks |
| topic | eigen problem eigen value artificial neural network back propagation |
| url | https://modelling.semnan.ac.ir/article_1658_da59110e7e04fb41acaa7cc247faee84.pdf |
| work_keys_str_mv | AT aliheidari approximateeigenvalueofplatebyartificialneuralnetworks AT davoudtavakoli approximateeigenvalueofplatebyartificialneuralnetworks AT pouyanfakharian approximateeigenvalueofplatebyartificialneuralnetworks |