Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm

There is doubtlessly manufactured artificial neural system (ANN) is a standout amongst the most acclaimed all-inclusive approximators, and has been executed in numerous fields. This is because of its capacity to naturally take in any example with no earlier suppositions and loss of all inclusive s...

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Main Authors:	Md. Ghani, Nor Azura, Ahmad Kamaruddin, Saadi, Mohamed Ramli, Norazan, Musirin, Ismail, Hashim, Hishamuddin
Format:	Article
Language:	English English
Published:	nstitute of Advanced Engineering and Science (IAES) 2017
Subjects:	Q Science (General) QA Mathematics
Online Access:	http://irep.iium.edu.my/62980/ http://irep.iium.edu.my/62980/ http://irep.iium.edu.my/62980/ http://irep.iium.edu.my/62980/1/62980_Modified%20BPNN%20via%20iterated%20least%20median%20squares_article.pdf http://irep.iium.edu.my/62980/2/62980_Modified%20BPNN%20via%20iterated%20least%20median%20squares_scopus.pdf

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spelling	iium-629802018-03-23T01:03:20Z http://irep.iium.edu.my/62980/ Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm Md. Ghani, Nor Azura Ahmad Kamaruddin, Saadi Mohamed Ramli, Norazan Musirin, Ismail Hashim, Hishamuddin Q Science (General) QA Mathematics There is doubtlessly manufactured artificial neural system (ANN) is a standout amongst the most acclaimed all-inclusive approximators, and has been executed in numerous fields. This is because of its capacity to naturally take in any example with no earlier suppositions and loss of all inclusive statement. ANNs have contributed fundamentally towards time arrangement expectation field, yet the nearness of exceptions that normally happen in the time arrangement information may dirty the system preparing information. Hypothetically, the most widely recognized calculation to prepare the system is the backpropagation (BP) calculation which depends on the minimization of the common ordinary least squares (OLS) estimator as far as mean squared error (MSE). Be that as it may, this calculation is not absolutely strong within the sight of exceptions and may bring about the bogus forecast of future qualities. Accordingly, in this paper, we actualize another calculation which exploits firefly calculation on the minimal middle of squares (FA-LMedS) estimator for manufactured neural system nonlinear autoregressive (BPNN-NAR) and counterfeit neural system nonlinear autoregressive moving normal (BPNN-NARMA) models to cook the different degrees of remote issue in time arrangement information. In addition, the execution of the proposed powerful estimator with correlation with the first MSE and strong iterative slightest middle squares (ILMedS) and molecule swarm advancement on minimum middle squares (PSOLMedS) estimators utilizing reenactment information, in light of root mean squared blunder (RMSE) are likewise talked about in this paper. It was found that the robustified backpropagation learning calculation utilizing FA-LMedS beat the first and other powerful estimators of ILMedS and PSO-LMedS. As a conclusion, developmental calculations beat the first MSE mistake capacity in giving hearty preparing of counterfeit neural systems. nstitute of Advanced Engineering and Science (IAES) 2017-12 Article PeerReviewed application/pdf en http://irep.iium.edu.my/62980/1/62980_Modified%20BPNN%20via%20iterated%20least%20median%20squares_article.pdf application/pdf en http://irep.iium.edu.my/62980/2/62980_Modified%20BPNN%20via%20iterated%20least%20median%20squares_scopus.pdf Md. Ghani, Nor Azura and Ahmad Kamaruddin, Saadi and Mohamed Ramli, Norazan and Musirin, Ismail and Hashim, Hishamuddin (2017) Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 8 (3). pp. 779-786. ISSN 2502-4752 E-ISSN 2502-4760 http://www.iaescore.com/journals/index.php/IJEECS/article/view/10006/7725 10.11591/ijeecs.v8.i3.pp779-786
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	Q Science (General) QA Mathematics
spellingShingle	Q Science (General) QA Mathematics Md. Ghani, Nor Azura Ahmad Kamaruddin, Saadi Mohamed Ramli, Norazan Musirin, Ismail Hashim, Hishamuddin Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm
description	There is doubtlessly manufactured artificial neural system (ANN) is a standout amongst the most acclaimed all-inclusive approximators, and has been executed in numerous fields. This is because of its capacity to naturally take in any example with no earlier suppositions and loss of all inclusive statement. ANNs have contributed fundamentally towards time arrangement expectation field, yet the nearness of exceptions that normally happen in the time arrangement information may dirty the system preparing information. Hypothetically, the most widely recognized calculation to prepare the system is the backpropagation (BP) calculation which depends on the minimization of the common ordinary least squares (OLS) estimator as far as mean squared error (MSE). Be that as it may, this calculation is not absolutely strong within the sight of exceptions and may bring about the bogus forecast of future qualities. Accordingly, in this paper, we actualize another calculation which exploits firefly calculation on the minimal middle of squares (FA-LMedS) estimator for manufactured neural system nonlinear autoregressive (BPNN-NAR) and counterfeit neural system nonlinear autoregressive moving normal (BPNN-NARMA) models to cook the different degrees of remote issue in time arrangement information. In addition, the execution of the proposed powerful estimator with correlation with the first MSE and strong iterative slightest middle squares (ILMedS) and molecule swarm advancement on minimum middle squares (PSOLMedS) estimators utilizing reenactment information, in light of root mean squared blunder (RMSE) are likewise talked about in this paper. It was found that the robustified backpropagation learning calculation utilizing FA-LMedS beat the first and other powerful estimators of ILMedS and PSO-LMedS. As a conclusion, developmental calculations beat the first MSE mistake capacity in giving hearty preparing of counterfeit neural systems.
format	Article
author	Md. Ghani, Nor Azura Ahmad Kamaruddin, Saadi Mohamed Ramli, Norazan Musirin, Ismail Hashim, Hishamuddin
author_facet	Md. Ghani, Nor Azura Ahmad Kamaruddin, Saadi Mohamed Ramli, Norazan Musirin, Ismail Hashim, Hishamuddin
author_sort	Md. Ghani, Nor Azura
title	Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm
title_short	Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm
title_full	Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm
title_fullStr	Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm
title_full_unstemmed	Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm
title_sort	modified bpnn via iterated least median squares, particle swarm optimization and firefly algorithm
publisher	nstitute of Advanced Engineering and Science (IAES)
publishDate	2017
url	http://irep.iium.edu.my/62980/ http://irep.iium.edu.my/62980/ http://irep.iium.edu.my/62980/ http://irep.iium.edu.my/62980/1/62980_Modified%20BPNN%20via%20iterated%20least%20median%20squares_article.pdf http://irep.iium.edu.my/62980/2/62980_Modified%20BPNN%20via%20iterated%20least%20median%20squares_scopus.pdf
first_indexed	2023-09-18T21:29:16Z
last_indexed	2023-09-18T21:29:16Z
_version_	1777412401003495424

Modified BPNN via iterated least median squares, particle Swarm optimization and firefly algorithm

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