PENERAPAN GLOBAL RIDGE-REGRESSION PADA PERAMALAN DATA TIME SERIES NON LINEAR STUDI KASUS : PEMODELAN NILAI TUKAR US DOLAR TERHADAP RUPIAH

ZULIANA, Sri Utami (2012) PENERAPAN GLOBAL RIDGE-REGRESSION PADA PERAMALAN DATA TIME SERIES NON LINEAR STUDI KASUS : PEMODELAN NILAI TUKAR US DOLAR TERHADAP RUPIAH. Kaunia Jurnal Sains dan Teknologi, V.VIII (No. 2). pp. 107-117. ISSN 2301-8550

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Abstract

Time series modelling has two types i.e. linear and non-linear. Feed Forward Neural Networks (FFNN) has modelled linear time series well but has found difficulties to model non-linear time series. Radial Basis Function Neural Networks (RBFNN) give an alternative to model non-linear time series. This network has Radial Basis Function in the hidden layer that provides non-linear functions. The RBFNN output is a linear combination of Radial Basis Functions and output weights. An optimal output has the least square error. The weights are gotten from regression. Global-ridge regression adds a regulation parameter to give the optimal weights that produce an optimal output. Keyword : Radial Basis Function Neural Networks (RBFNN), non-linear, time series, global ridge-regression

Item Type: Article
Uncontrolled Keywords: Radial Basis Function Neural Networks (RBFNN), non-linear, time series, global ridge-regression
Subjects: Kaunia Jurnal
Divisions: Artikel (Terbitan Luar UIN)
Depositing User / Editor: Sugeng Hariyanto, SIP (sugeng.hariyanto@uin-suka.ac.id)
Date Deposited: 18 Dec 2015 03:09
Last Modified: 18 Dec 2015 03:09
URI: http://digilib.uin-suka.ac.id/id/eprint/18718

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