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Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking

One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensive...

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Autores principales: Kaya, Ebubekir, Baştemur Kaya, Ceren, Bendeş, Emre, Atasever, Sema, Öztürk, Başak, Yazlık, Bilgin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10526777/
https://www.ncbi.nlm.nih.gov/pubmed/37754153
http://dx.doi.org/10.3390/biomimetics8050402
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author Kaya, Ebubekir
Baştemur Kaya, Ceren
Bendeş, Emre
Atasever, Sema
Öztürk, Başak
Yazlık, Bilgin
author_facet Kaya, Ebubekir
Baştemur Kaya, Ceren
Bendeş, Emre
Atasever, Sema
Öztürk, Başak
Yazlık, Bilgin
author_sort Kaya, Ebubekir
collection PubMed
description One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization algorithms. These algorithms are artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization. Mean squared error is used as the error metric, and the performances of the algorithms in different network structures are evaluated. Considering the results, a success ranking score is obtained for each algorithm. The three most successful algorithms in both training and testing processes are the firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm, respectively. The training error values obtained with these algorithms are 4.5 × 10(−4), 1.6 × 10(−3), and 2.3 × 10(−3), respectively. The test error values are 4.6 × 10(−4), 1.6 × 10(−3), and 2.4 × 10(−3), respectively. With these algorithms, effective results have been achieved in a low number of evaluations. In addition to these three algorithms, other algorithms have also achieved mostly acceptable results. This shows that the related algorithms are generally successful ANFIS training algorithms for maximum power point tracking.
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spelling pubmed-105267772023-09-28 Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking Kaya, Ebubekir Baştemur Kaya, Ceren Bendeş, Emre Atasever, Sema Öztürk, Başak Yazlık, Bilgin Biomimetics (Basel) Article One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization algorithms. These algorithms are artificial bee colony, butterfly optimization, cuckoo search, chicken swarm optimization, dragonfly algorithm, firefly algorithm, grasshopper optimization algorithm, krill herd algorithm, particle swarm optimization, salp swarm algorithm, selfish herd optimizer, tunicate swarm algorithm, and tuna swarm optimization. Mean squared error is used as the error metric, and the performances of the algorithms in different network structures are evaluated. Considering the results, a success ranking score is obtained for each algorithm. The three most successful algorithms in both training and testing processes are the firefly algorithm, selfish herd optimizer, and grasshopper optimization algorithm, respectively. The training error values obtained with these algorithms are 4.5 × 10(−4), 1.6 × 10(−3), and 2.3 × 10(−3), respectively. The test error values are 4.6 × 10(−4), 1.6 × 10(−3), and 2.4 × 10(−3), respectively. With these algorithms, effective results have been achieved in a low number of evaluations. In addition to these three algorithms, other algorithms have also achieved mostly acceptable results. This shows that the related algorithms are generally successful ANFIS training algorithms for maximum power point tracking. MDPI 2023-09-01 /pmc/articles/PMC10526777/ /pubmed/37754153 http://dx.doi.org/10.3390/biomimetics8050402 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kaya, Ebubekir
Baştemur Kaya, Ceren
Bendeş, Emre
Atasever, Sema
Öztürk, Başak
Yazlık, Bilgin
Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking
title Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking
title_full Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking
title_fullStr Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking
title_full_unstemmed Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking
title_short Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking
title_sort training of feed-forward neural networks by using optimization algorithms based on swarm-intelligent for maximum power point tracking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10526777/
https://www.ncbi.nlm.nih.gov/pubmed/37754153
http://dx.doi.org/10.3390/biomimetics8050402
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