A method for parameter estimation in the NARMAX model with ARCH errors by RBF networks

Author

Asato, H. ; Yamashita, Katsumi ; Miyagi, Hayao

Author_Institution

Dept. of Electr. & Electron. Eng., Ryukyus Univ., Okinawa, Japan

Volume

5

fYear

1999

fDate

1999

Firstpage

425

Abstract

A multilayer neural network is used in a number of applications such as the prediction problem of nonlinear time series. A radial basis functions (RBF) model, which can be viewed as a three-layer NN with a linear output mapping, is capable of modeling a wide class of nonlinear functions with arbitrary accuracy. We demonstrate that it is feasible to identify a NARMAX-like structure by RBF networks, and to use this architecture for predicting the nonlinear time series (NARX model) with ARCH (autoregressive conditional heteroskedastic) errors. The effectiveness of the proposed RBF networks system is evaluated using computer simulation

Keywords

autoregressive processes; multilayer perceptrons; parameter estimation; radial basis function networks; time series; ARCH errors; NARMAX model; RBF networks; autoregressive conditional heteroskedastic errors; linear output mapping; multilayer neural network; nonlinear time series; radial basis functions model; Computer architecture; Econometrics; Economic forecasting; Intelligent networks; Macroeconomics; Multi-layer neural network; Neural networks; Parameter estimation; Predictive models; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on

Conference_Location

Tokyo

ISSN

1062-922X

Print_ISBN

0-7803-5731-0

Type

conf

DOI

10.1109/ICSMC.1999.815588

Filename

815588