Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques

Neural system procedures have a colossal reputation in the space of gauging. In any case, there is yet to be a sure strategy that can well accept the last model of the neural system time arrangement demonstrating. Thus, this paper propose a way to deal with accepting the said displaying utilizing ti...

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Main Authors:	Md. Ghani, Nor Azura, Ahmad Kamaruddin, Saadi, Mohamed Ramli, Norazan, Selamat, Ali
Format:	Conference or Workshop Item
Language:	English English English
Published:	Springer, Cham 2017
Subjects:	RB Pathology RD Surgery RG Gynecology and obstetrics
Online Access:	http://irep.iium.edu.my/56975/ http://irep.iium.edu.my/56975/ http://irep.iium.edu.my/56975/ http://irep.iium.edu.my/56975/19/56975_Authenticating%20ANN-NAR%20and%20ANN-NARMA_complete.pdf http://irep.iium.edu.my/56975/2/56975_Authenticating%20ANN-NAR_SCOPUS.pdf http://irep.iium.edu.my/56975/13/56975%20Authenticating%20ANN-NAR%20and%20ANN-NARMA%20WOS.pdf

id	iium-56975
recordtype	eprints
spelling	iium-569752019-08-27T01:52:29Z http://irep.iium.edu.my/56975/ Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques Md. Ghani, Nor Azura Ahmad Kamaruddin, Saadi Mohamed Ramli, Norazan Selamat, Ali RB Pathology RD Surgery RG Gynecology and obstetrics Neural system procedures have a colossal reputation in the space of gauging. In any case, there is yet to be a sure strategy that can well accept the last model of the neural system time arrangement demonstrating. Thus, this paper propose a way to deal with accepting the said displaying utilizing time arrangement square bootstrap. This straightforward technique is different compared to the traditional piece bootstrap of time-arrangement based, where it was composed by making utilization of every information set in the information apportioning procedure of neural system demonstrating; preparing set, testing set and approval set. At this point, every information set was separated into two little squares, called the odd and even pieces (non-covering pieces). At that point, from every piece, an arbitrary inspecting with substitution in a rising structure was made, and these duplicated tests can be named as odd-even square bootstrap tests. In time, the examples were executed in the neural system preparing for last voted expectation yield. The proposed strategy was forced on both manufactured neural system time arrangement models, which were nonlinear autoregressive (NAR) and nonlinear autoregressive moving normal (NARMA). In this study, three changing genuine modern month to month information of Malaysian development materials value records from January 1980 to December 2012 were utilized. It was found that the suggested bootstrapped neural system time arrangement models beat the first neural system time arrangement models. Springer, Cham 2017-02-26 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/56975/19/56975_Authenticating%20ANN-NAR%20and%20ANN-NARMA_complete.pdf application/pdf en http://irep.iium.edu.my/56975/2/56975_Authenticating%20ANN-NAR_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/56975/13/56975%20Authenticating%20ANN-NAR%20and%20ANN-NARMA%20WOS.pdf Md. Ghani, Nor Azura and Ahmad Kamaruddin, Saadi and Mohamed Ramli, Norazan and Selamat, Ali (2017) Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques. In: 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2017), 3rd-5th April 2017, Kanazawa, Japan. https://link.springer.com/chapter/10.1007/978-3-319-54472-4_71 10.1007/978-3-319-54472-4_71
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	RB Pathology RD Surgery RG Gynecology and obstetrics
spellingShingle	RB Pathology RD Surgery RG Gynecology and obstetrics Md. Ghani, Nor Azura Ahmad Kamaruddin, Saadi Mohamed Ramli, Norazan Selamat, Ali Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques
description	Neural system procedures have a colossal reputation in the space of gauging. In any case, there is yet to be a sure strategy that can well accept the last model of the neural system time arrangement demonstrating. Thus, this paper propose a way to deal with accepting the said displaying utilizing time arrangement square bootstrap. This straightforward technique is different compared to the traditional piece bootstrap of time-arrangement based, where it was composed by making utilization of every information set in the information apportioning procedure of neural system demonstrating; preparing set, testing set and approval set. At this point, every information set was separated into two little squares, called the odd and even pieces (non-covering pieces). At that point, from every piece, an arbitrary inspecting with substitution in a rising structure was made, and these duplicated tests can be named as odd-even square bootstrap tests. In time, the examples were executed in the neural system preparing for last voted expectation yield. The proposed strategy was forced on both manufactured neural system time arrangement models, which were nonlinear autoregressive (NAR) and nonlinear autoregressive moving normal (NARMA). In this study, three changing genuine modern month to month information of Malaysian development materials value records from January 1980 to December 2012 were utilized. It was found that the suggested bootstrapped neural system time arrangement models beat the first neural system time arrangement models.
format	Conference or Workshop Item
author	Md. Ghani, Nor Azura Ahmad Kamaruddin, Saadi Mohamed Ramli, Norazan Selamat, Ali
author_facet	Md. Ghani, Nor Azura Ahmad Kamaruddin, Saadi Mohamed Ramli, Norazan Selamat, Ali
author_sort	Md. Ghani, Nor Azura
title	Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques
title_short	Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques
title_full	Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques
title_fullStr	Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques
title_full_unstemmed	Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques
title_sort	authenticating ann-nar and ann-narma models utilizing bootstrap techniques
publisher	Springer, Cham
publishDate	2017
url	http://irep.iium.edu.my/56975/ http://irep.iium.edu.my/56975/ http://irep.iium.edu.my/56975/ http://irep.iium.edu.my/56975/19/56975_Authenticating%20ANN-NAR%20and%20ANN-NARMA_complete.pdf http://irep.iium.edu.my/56975/2/56975_Authenticating%20ANN-NAR_SCOPUS.pdf http://irep.iium.edu.my/56975/13/56975%20Authenticating%20ANN-NAR%20and%20ANN-NARMA%20WOS.pdf
first_indexed	2023-09-18T21:20:28Z
last_indexed	2023-09-18T21:20:28Z
_version_	1777411847681474560

Authenticating ANN-NAR and ANN-NARMA models utilizing bootstrap techniques

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