Parameter prediction for Lorenz Attractor by using Deep Neural Network
Nowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characteriz...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Advanced Engineering and Science
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30470/1/Parameter%20Prediction%20for%20Lorenz%20Attractor.pdf |
| _version_ | 1796994435002662912 |
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| author | Nurnajmin Qasrina Ann, Ayop Azmi Pebrianti, Dwi Mohammad Fadhil, Abas Bayuaji, Luhur Syafrullah, Muhammad |
| author_facet | Nurnajmin Qasrina Ann, Ayop Azmi Pebrianti, Dwi Mohammad Fadhil, Abas Bayuaji, Luhur Syafrullah, Muhammad |
| author_sort | Nurnajmin Qasrina Ann, Ayop Azmi |
| collection | UMP |
| description | Nowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characterized by an unstable dynamic behavior. The research aims to predict the parameter of a strange Lorenz attractor either yes or not. The primary method implemented in this paper is the Deep Neural Network by using Phyton Keras library. For the neural network, the different number of hidden layers are used to compare the accuracy of the system prediction. A set of data is used as the input of the neural network, while for the output part, the accuracy of prediction data is expected. As a result, the accuracy of the testing result shows that 100% correct prediction can be achieved when using the training data. Meanwhile, only 60% correct prediction is achieved for the new random data.
, , Abas, Bayuaji, Mohammad |
| first_indexed | 2024-03-06T12:47:45Z |
| format | Article |
| id | UMPir30470 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:47:45Z |
| publishDate | 2020 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | UMPir304702021-01-11T08:24:49Z http://umpir.ump.edu.my/id/eprint/30470/ Parameter prediction for Lorenz Attractor by using Deep Neural Network Nurnajmin Qasrina Ann, Ayop Azmi Pebrianti, Dwi Mohammad Fadhil, Abas Bayuaji, Luhur Syafrullah, Muhammad QA75 Electronic computers. Computer science Nowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characterized by an unstable dynamic behavior. The research aims to predict the parameter of a strange Lorenz attractor either yes or not. The primary method implemented in this paper is the Deep Neural Network by using Phyton Keras library. For the neural network, the different number of hidden layers are used to compare the accuracy of the system prediction. A set of data is used as the input of the neural network, while for the output part, the accuracy of prediction data is expected. As a result, the accuracy of the testing result shows that 100% correct prediction can be achieved when using the training data. Meanwhile, only 60% correct prediction is achieved for the new random data. , , Abas, Bayuaji, Mohammad Institute of Advanced Engineering and Science 2020 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/30470/1/Parameter%20Prediction%20for%20Lorenz%20Attractor.pdf Nurnajmin Qasrina Ann, Ayop Azmi and Pebrianti, Dwi and Mohammad Fadhil, Abas and Bayuaji, Luhur and Syafrullah, Muhammad (2020) Parameter prediction for Lorenz Attractor by using Deep Neural Network. Indonesian Journal of Electrical Engineering and Informatics, 8 (3). pp. 532-540. ISSN 2089-3272. (Published) http://section.iaesonline.com/index.php/IJEEI/article/view/1272 DOI: 10.11591/ijeei.v8i3.1272 |
| spellingShingle | QA75 Electronic computers. Computer science Nurnajmin Qasrina Ann, Ayop Azmi Pebrianti, Dwi Mohammad Fadhil, Abas Bayuaji, Luhur Syafrullah, Muhammad Parameter prediction for Lorenz Attractor by using Deep Neural Network |
| title | Parameter prediction for Lorenz Attractor by using Deep Neural Network |
| title_full | Parameter prediction for Lorenz Attractor by using Deep Neural Network |
| title_fullStr | Parameter prediction for Lorenz Attractor by using Deep Neural Network |
| title_full_unstemmed | Parameter prediction for Lorenz Attractor by using Deep Neural Network |
| title_short | Parameter prediction for Lorenz Attractor by using Deep Neural Network |
| title_sort | parameter prediction for lorenz attractor by using deep neural network |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/30470/1/Parameter%20Prediction%20for%20Lorenz%20Attractor.pdf |
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