The Impact of Learning rate on Backpropagation Algorithm in Matlab
Artificial Neural Networks (ANNs) are highly interconnected. Backpropagation is a common method for training artificial neural networks to minimize the objective function. This study describes the implementation of the backpropagation algorithm. The different errors generated at the output are fed...
| Published in: | Pakistan Journal of Engineering Technology & Science |
|---|---|
| Main Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Business Management
2023-12-01
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| Subjects: | |
| Online Access: | https://journals.iobm.edu.pk/index.php/pjets/article/view/1014 |
| _version_ | 1848664583928020992 |
|---|---|
| author | Abdul Ghafoor Shaikh Wajid Ali Shaikh |
| author_facet | Abdul Ghafoor Shaikh Wajid Ali Shaikh |
| author_sort | Abdul Ghafoor Shaikh |
| collection | DOAJ |
| container_title | Pakistan Journal of Engineering Technology & Science |
| description |
Artificial Neural Networks (ANNs) are highly interconnected. Backpropagation is a common method for training artificial neural networks to minimize the objective function. This study describes the implementation of the backpropagation algorithm. The different errors generated at the output are fed back to the input, and the weights of the neurons are updated by different supervised learning rates, which is a generalization of the delta rule. A sigmoid function was used as the activation function. The design was simulated using MATLAB R2018a. The maximum accuracy was achieved 0.9988 with four hidden layers
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| format | Article |
| id | doaj-art-e5487b4bbb6f4d479fd65ab3a5cbf830 |
| institution | Directory of Open Access Journals |
| issn | 2222-9930 2224-2333 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Institute of Business Management |
| record_format | Article |
| spelling | doaj-art-e5487b4bbb6f4d479fd65ab3a5cbf8302025-10-31T00:01:23ZengInstitute of Business ManagementPakistan Journal of Engineering Technology & Science2222-99302224-23332023-12-0111210.22555/pjets.v11i2.1014The Impact of Learning rate on Backpropagation Algorithm in MatlabAbdul Ghafoor ShaikhWajid Ali Shaikh Artificial Neural Networks (ANNs) are highly interconnected. Backpropagation is a common method for training artificial neural networks to minimize the objective function. This study describes the implementation of the backpropagation algorithm. The different errors generated at the output are fed back to the input, and the weights of the neurons are updated by different supervised learning rates, which is a generalization of the delta rule. A sigmoid function was used as the activation function. The design was simulated using MATLAB R2018a. The maximum accuracy was achieved 0.9988 with four hidden layers https://journals.iobm.edu.pk/index.php/pjets/article/view/1014Artificial Neural NetworkBackpropagation AlgorithmHidden LayerSigmoid |
| spellingShingle | Abdul Ghafoor Shaikh Wajid Ali Shaikh The Impact of Learning rate on Backpropagation Algorithm in Matlab Artificial Neural Network Backpropagation Algorithm Hidden Layer Sigmoid |
| title | The Impact of Learning rate on Backpropagation Algorithm in Matlab |
| title_full | The Impact of Learning rate on Backpropagation Algorithm in Matlab |
| title_fullStr | The Impact of Learning rate on Backpropagation Algorithm in Matlab |
| title_full_unstemmed | The Impact of Learning rate on Backpropagation Algorithm in Matlab |
| title_short | The Impact of Learning rate on Backpropagation Algorithm in Matlab |
| title_sort | impact of learning rate on backpropagation algorithm in matlab |
| topic | Artificial Neural Network Backpropagation Algorithm Hidden Layer Sigmoid |
| url | https://journals.iobm.edu.pk/index.php/pjets/article/view/1014 |
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