Enhanced BFGS quasi-newton backpropagation models on MCCI data

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 E-ISSN 2502-4760

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Abstract

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

Item Type:	Article (Journal)
Additional Information:	7581/62831
Uncontrolled Keywords:	Outliers, CCI data, trainbfg, robust estimators, evolutionary algorithms
Subjects:	Q Science > Q Science (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Science Kulliyyah of Science > Department of Computational and Theoretical Sciences
Depositing User:	Dr Saadi Kamaruddin
Date Deposited:	20 Mar 2018 12:25
Last Modified:	20 Apr 2018 14:08
URI:	http://irep.iium.edu.my/id/eprint/62831

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