A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction

Hydrogeneration prediction typically has composite structures such as nonlinearity, non-stationarity, and fluctuation, which converts its predicting to be very tough. The applications of backpropagation neural network (BPNN) are very various and saturated. The linear threshold part of the BPNN produ...

Full description

Bibliographic Details
Main Authors:	Hammid, Ali Thaeer, M. H., Sulaiman, Awad, Omar I.
Format:	Article
Language:	English
Published:	Springer 2018
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/23312/ http://umpir.ump.edu.my/id/eprint/23312/ http://umpir.ump.edu.my/id/eprint/23312/ http://umpir.ump.edu.my/id/eprint/23312/1/A%20robust%20firefly%20algorithm%20with%20backpropagation%20neural%20networks%20for%20solving%20hydrogeneration%20prediction%20-%20s00202-018-0732-6.pdf

id	ump-23312
recordtype	eprints
spelling	ump-233122019-02-28T03:29:13Z http://umpir.ump.edu.my/id/eprint/23312/ A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction Hammid, Ali Thaeer M. H., Sulaiman Awad, Omar I. TK Electrical engineering. Electronics Nuclear engineering Hydrogeneration prediction typically has composite structures such as nonlinearity, non-stationarity, and fluctuation, which converts its predicting to be very tough. The applications of backpropagation neural network (BPNN) are very various and saturated. The linear threshold part of the BPNN produces rapid learning with bounded abilities, also the procedure of BPNN causes the slow speed of training. The objective of this study, first, a firefly algorithm (FA) based on the k-fold cross-validation of BPNN has been suggested to predict data for keeping rapid learning and prevents the exponential increase in operating parts. Second, it is to construct on this method to improve an efficient process for prediction problems that can discover efficient solutions at a high speed of convergence. For this purpose, the suggested approach that makes a hybridizing the FA with the robust algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. The algorithms were verified on an original dataset of the Himreen Lake Dam. The results display that the regression coefficient, root-mean-square error, mean absolute error, and mean bias error values of the suggested model are 99.86%, 1.87%, 0.91%, and 0.31%, respectively. Furthermore, the performance of the suggested robust firefly algorithm model is better than previously mentioned models in terms of speed and accuracy of prediction. Springer 2018-12-06 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23312/1/A%20robust%20firefly%20algorithm%20with%20backpropagation%20neural%20networks%20for%20solving%20hydrogeneration%20prediction%20-%20s00202-018-0732-6.pdf Hammid, Ali Thaeer and M. H., Sulaiman and Awad, Omar I. (2018) A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction. Electrical Engineering, 100 (4). pp. 2617-2633. ISSN 0948-7921 (Print); 1432-0487 (Online) https://link.springer.com/article/10.1007/s00202-018-0732-6 https://doi.org/10.1007/s00202-018-0732-6
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Hammid, Ali Thaeer M. H., Sulaiman Awad, Omar I. A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction
description	Hydrogeneration prediction typically has composite structures such as nonlinearity, non-stationarity, and fluctuation, which converts its predicting to be very tough. The applications of backpropagation neural network (BPNN) are very various and saturated. The linear threshold part of the BPNN produces rapid learning with bounded abilities, also the procedure of BPNN causes the slow speed of training. The objective of this study, first, a firefly algorithm (FA) based on the k-fold cross-validation of BPNN has been suggested to predict data for keeping rapid learning and prevents the exponential increase in operating parts. Second, it is to construct on this method to improve an efficient process for prediction problems that can discover efficient solutions at a high speed of convergence. For this purpose, the suggested approach that makes a hybridizing the FA with the robust algorithm (RA), where RA is used to control the steps of randomness for the FA while optimizing the weights of the standard BPNN model. The algorithms were verified on an original dataset of the Himreen Lake Dam. The results display that the regression coefficient, root-mean-square error, mean absolute error, and mean bias error values of the suggested model are 99.86%, 1.87%, 0.91%, and 0.31%, respectively. Furthermore, the performance of the suggested robust firefly algorithm model is better than previously mentioned models in terms of speed and accuracy of prediction.
format	Article
author	Hammid, Ali Thaeer M. H., Sulaiman Awad, Omar I.
author_facet	Hammid, Ali Thaeer M. H., Sulaiman Awad, Omar I.
author_sort	Hammid, Ali Thaeer
title	A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction
title_short	A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction
title_full	A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction
title_fullStr	A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction
title_full_unstemmed	A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction
title_sort	robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction
publisher	Springer
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23312/ http://umpir.ump.edu.my/id/eprint/23312/ http://umpir.ump.edu.my/id/eprint/23312/ http://umpir.ump.edu.my/id/eprint/23312/1/A%20robust%20firefly%20algorithm%20with%20backpropagation%20neural%20networks%20for%20solving%20hydrogeneration%20prediction%20-%20s00202-018-0732-6.pdf
first_indexed	2023-09-18T22:34:50Z
last_indexed	2023-09-18T22:34:50Z
_version_	1777416526284980224

A robust firefly algorithm with backpropagation neural networks for solving hydrogeneration prediction

Similar Items