%A Sri Utami ZULIANA
%J Kaunia Jurnal Sains dan Teknologi
%T PENERAPAN GLOBAL RIDGE-REGRESSION PADA PERAMALAN DATA TIME SERIES NON LINEAR STUDI KASUS : PEMODELAN NILAI TUKAR US DOLAR TERHADAP RUPIAH
%X 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
%N No. 2
%K Radial Basis Function Neural Networks (RBFNN), non-linear, time series, global ridge-regression
%P 107-117
%V V.VIII
%D 2012
%I FAK. SAINTEK UIN SUNAN KALIJAGA
%R 2301-8550
%L digilib18718