14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper FrB1.4

Haggan-Ozaki, Valerie (Sophia Univ.), Ozaki, Tohru (Inst. of Statistical Mathematics), Toyoda, Yukihiro (Niihama National Coll. of Tech.)

RBF-ARX Modeling for Prediction and Control

Scheduled for presentation during the Regular Session "Kernel Based Nonlinear System Identification" (FrB1), Friday, March 31, 2006, 16:30−16:50, Concert Hall

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on September 24, 2018

Keywords Nonlinear System Identification, Identification for Control, Time Series


This paper presents a systematic approach to the complex problem of RBF-ARX modeling. First, we point out that many of the nonlinear features of a time series may be represented by a relatively simple RBF-ARX model. A method for estimating the number of RBF centers is then proposed based on the behavior of the state variable, and initial values for the centers are also found. Linear estimation methods are implemented to select the initial lag orders of candidate models. Model parameters are found by nonlinear estimation and candidate models are compared using AIC, SBC criteria and other diagnostic checks. The modeling approach is shown to work well in practice by estimating optimum RBF-ARX models for real and simulated time series data and comparing the results with those of previous authors. Diagnostic checking also confirms the validity of the method.