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Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction
Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. To...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8459779/ https://www.ncbi.nlm.nih.gov/pubmed/34616896 http://dx.doi.org/10.7717/peerj-cs.724 |
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author | Rachmatullah, Muhammad Ibnu Choldun Santoso, Judhi Surendro, Kridanto |
author_facet | Rachmatullah, Muhammad Ibnu Choldun Santoso, Judhi Surendro, Kridanto |
author_sort | Rachmatullah, Muhammad Ibnu Choldun |
collection | PubMed |
description | Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. Topology determination means finding the hidden layers number and the hidden neurons number for corresponding hidden layer in the neural network. The difference between this research and previous research is that the objective function of this research is regression, while the objective function of previous research is classification. Determination of the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used to predict wind speed. Then the performance of topology determination using PCA and clustering is then compared with several other methods. The results of the experiment show that the performance of the neural network topology determined using PCA and clustering has better performance than the other methods being compared. Performance is determined based on the RMSE value, the smaller the RMSE value, the better the neural network performance. In future research, it is necessary to apply a correlation or relationship between input attribute and output attribute and then analyzed, prior to conducting PCA and clustering analysis. |
format | Online Article Text |
id | pubmed-8459779 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84597792021-10-05 Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction Rachmatullah, Muhammad Ibnu Choldun Santoso, Judhi Surendro, Kridanto PeerJ Comput Sci Artificial Intelligence Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. Topology determination means finding the hidden layers number and the hidden neurons number for corresponding hidden layer in the neural network. The difference between this research and previous research is that the objective function of this research is regression, while the objective function of previous research is classification. Determination of the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used to predict wind speed. Then the performance of topology determination using PCA and clustering is then compared with several other methods. The results of the experiment show that the performance of the neural network topology determined using PCA and clustering has better performance than the other methods being compared. Performance is determined based on the RMSE value, the smaller the RMSE value, the better the neural network performance. In future research, it is necessary to apply a correlation or relationship between input attribute and output attribute and then analyzed, prior to conducting PCA and clustering analysis. PeerJ Inc. 2021-09-20 /pmc/articles/PMC8459779/ /pubmed/34616896 http://dx.doi.org/10.7717/peerj-cs.724 Text en ©2021 Rachmatullah et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Rachmatullah, Muhammad Ibnu Choldun Santoso, Judhi Surendro, Kridanto Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction |
title | Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction |
title_full | Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction |
title_fullStr | Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction |
title_full_unstemmed | Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction |
title_short | Determining the number of hidden layer and hidden neuron of neural network for wind speed prediction |
title_sort | determining the number of hidden layer and hidden neuron of neural network for wind speed prediction |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8459779/ https://www.ncbi.nlm.nih.gov/pubmed/34616896 http://dx.doi.org/10.7717/peerj-cs.724 |
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