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

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.

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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.

Item Type: Conference or Workshop Item (Plenary Papers)
Additional Information: 7581/56975
Uncontrolled Keywords: Malaysian construction material price indices, Neural networks, Non-overlapping block bootstrap, Time series prediction
Subjects: R Medicine > RB Pathology
R Medicine > RD Surgery
R Medicine > RG Gynecology and obstetrics
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Science > Department of Computational and Theoretical Sciences
Depositing User: Dr Saadi Kamaruddin
Date Deposited: 27 Aug 2019 09:52
Last Modified: 27 Aug 2019 09:52
URI: http://irep.iium.edu.my/id/eprint/56975

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