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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50013657" target="_blank" >RIV/62690094:18450/17:50013657 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007%2F978-3-319-54472-4_71" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-319-54472-4_71</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-54472-4_71" target="_blank" >10.1007/978-3-319-54472-4_71</a>

Result language

angličtina

Original language name

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

Original language 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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Springer LNCS

ISBN

978-3-319-54471-7

ISSN

0302-9743

e-ISSN

neuvedeno

Number of pages

11

Pages from-to

761-771

Publisher name

Springer

Place of publication

Cham

Event location

Kanazawa, Japan

Event date

Apr 3, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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