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Enhanced BFGS quasi-newton backpropagation models on MCCI data

Neurocomputing is widely implemented in time series area, however the nearness of exceptions that for the most part happen in information time arrangement might be hurtful to the information organize preparing. This is on the grounds that the capacity to consequently discover any examples without...

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Main Authors: Md. Ghani, Nor Azura, Kamaruddin, Saadi, Mohamed Ramli, Norazan, Musirin, Ismail, Hashim, Hishamuddin
Format: Article
Language:English
English
Published: Institute of Advanced Engineering and Science (IAES) 2017
Subjects:
Q Science (General)
Online Access:http://irep.iium.edu.my/62831/1/52831_Enhanced%20BFGS%20quasi-newton%20backpropagation_article.pdf
http://irep.iium.edu.my/62831/2/52831_Enhanced%20BFGS%20quasi-newton%20backpropagation_scopus.pdf
http://irep.iium.edu.my/62831/
http://www.iaescore.com/journals/index.php/IJEECS/article/viewFile/9735/7597
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spelling my.iium.irep.628312018-04-20T06:08:58Z http://irep.iium.edu.my/62831/ Enhanced BFGS quasi-newton backpropagation models on MCCI data Md. Ghani, Nor Azura Kamaruddin, Saadi Mohamed Ramli, Norazan Musirin, Ismail Hashim, Hishamuddin Q Science (General) Neurocomputing is widely implemented in time series area, however the nearness of exceptions that for the most part happen in information time arrangement might be hurtful to the information organize preparing. This is on the grounds that the capacity to consequently discover any examples without earlier suppositions and loss of all-inclusive statement. In principle, the most well-known preparing calculation for Backpropagation calculations inclines toward lessening ordinary least squares estimator (OLS) or all the more particularly, the mean squared error (MSE). In any case, this calculation is not completely hearty when exceptions exist in preparing information, and it will prompt false estimate future esteem. Along these lines, in this paper, we show another calculation that control calculations firefly on slightest middle squares estimator (FFA-LMedS) for BFGS quasi-newton backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) model to lessen the effect of exceptions in time arrangement information. In the in the mean time, the monthly data of Malaysian Roof Materials cost index from January 1980 to December 2012 (base year 1980=100) with various level of exceptions issue is adjusted in this examination. Toward the finish of this paper, it was found that the upgraded BPNN-NARMA models utilizing FFA-LMedS performed extremely well with RMSE values just about zero errors. It is expected that the finding would help the specialists in Malaysian development activities to handle cost indices data accordingly Institute of Advanced Engineering and Science (IAES) 2017-10 Article REM application/pdf en http://irep.iium.edu.my/62831/1/52831_Enhanced%20BFGS%20quasi-newton%20backpropagation_article.pdf application/pdf en http://irep.iium.edu.my/62831/2/52831_Enhanced%20BFGS%20quasi-newton%20backpropagation_scopus.pdf Md. Ghani, Nor Azura and Kamaruddin, Saadi and Mohamed Ramli, Norazan and Musirin, Ismail and Hashim, Hishamuddin (2017) Enhanced BFGS quasi-newton backpropagation models on MCCI data. Indonesian Journal of Electrical Engineering and Computer Science, 8 (1). pp. 101-106. ISSN 2502-4752 http://www.iaescore.com/journals/index.php/IJEECS/article/viewFile/9735/7597 10.11591/ijeecs.v8.i1.pp101-106
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic Q Science (General)
spellingShingle Q Science (General)
Md. Ghani, Nor Azura
Kamaruddin, Saadi
Mohamed Ramli, Norazan
Musirin, Ismail
Hashim, Hishamuddin
Enhanced BFGS quasi-newton backpropagation models on MCCI data
description Neurocomputing is widely implemented in time series area, however the nearness of exceptions that for the most part happen in information time arrangement might be hurtful to the information organize preparing. This is on the grounds that the capacity to consequently discover any examples without earlier suppositions and loss of all-inclusive statement. In principle, the most well-known preparing calculation for Backpropagation calculations inclines toward lessening ordinary least squares estimator (OLS) or all the more particularly, the mean squared error (MSE). In any case, this calculation is not completely hearty when exceptions exist in preparing information, and it will prompt false estimate future esteem. Along these lines, in this paper, we show another calculation that control calculations firefly on slightest middle squares estimator (FFA-LMedS) for BFGS quasi-newton backpropagation neural network nonlinear autoregressive moving (BPNN-NARMA) model to lessen the effect of exceptions in time arrangement information. In the in the mean time, the monthly data of Malaysian Roof Materials cost index from January 1980 to December 2012 (base year 1980=100) with various level of exceptions issue is adjusted in this examination. Toward the finish of this paper, it was found that the upgraded BPNN-NARMA models utilizing FFA-LMedS performed extremely well with RMSE values just about zero errors. It is expected that the finding would help the specialists in Malaysian development activities to handle cost indices data accordingly
format Article
author Md. Ghani, Nor Azura
Kamaruddin, Saadi
Mohamed Ramli, Norazan
Musirin, Ismail
Hashim, Hishamuddin
author_facet Md. Ghani, Nor Azura
Kamaruddin, Saadi
Mohamed Ramli, Norazan
Musirin, Ismail
Hashim, Hishamuddin
author_sort Md. Ghani, Nor Azura
title Enhanced BFGS quasi-newton backpropagation models on MCCI data
title_short Enhanced BFGS quasi-newton backpropagation models on MCCI data
title_full Enhanced BFGS quasi-newton backpropagation models on MCCI data
title_fullStr Enhanced BFGS quasi-newton backpropagation models on MCCI data
title_full_unstemmed Enhanced BFGS quasi-newton backpropagation models on MCCI data
title_sort enhanced bfgs quasi-newton backpropagation models on mcci data
publisher Institute of Advanced Engineering and Science (IAES)
publishDate 2017
url http://irep.iium.edu.my/62831/1/52831_Enhanced%20BFGS%20quasi-newton%20backpropagation_article.pdf
http://irep.iium.edu.my/62831/2/52831_Enhanced%20BFGS%20quasi-newton%20backpropagation_scopus.pdf
http://irep.iium.edu.my/62831/
http://www.iaescore.com/journals/index.php/IJEECS/article/viewFile/9735/7597
_version_ 1643616249022775296
score 13.211869

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