relation: https://digilib.uin-suka.ac.id/id/eprint/18718/
title: PENERAPAN GLOBAL RIDGE-REGRESSION PADA PERAMALAN DATA TIME SERIES NON LINEAR STUDI KASUS : PEMODELAN NILAI TUKAR US DOLAR TERHADAP RUPIAH
creator: ZULIANA, Sri Utami
subject: Kaunia Jurnal
description: 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
publisher: FAK. SAINTEK UIN SUNAN KALIJAGA
date: 2012-10-01
type: Article
type: PeerReviewed
format: text
language: id
identifier: https://digilib.uin-suka.ac.id/id/eprint/18718/1/04-Kaunia-Vol.VIII-No.2-Sri-Utami-Zuliana-Penerapan-Global-ridge-regression-pada-Peramalan-Data-Time-Series-Non-Linear.pdf
identifier:   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     
relation: 2301-8550