Constructing NARMAX models using ARMAX models
- 1 November 1993
- journal article
- research article
- Published by Taylor & Francis in International Journal of Control
- Vol. 58 (5) , 1125-1153
- https://doi.org/10.1080/00207179308923046
Abstract
This paper outlines how it is possible to decompose a complex non-linear modelling problem into a set of simpler linear modelling problems. Local ARMAX models valid within certain operating regimes are interpolated to construct a global NARMAX (non-linear NARMAX) model. Knowledge of the system behaviour in terms of operating regimes is the primary basis for building such models, hence it should not be considered as a pure black-box approach, but as an approach that utilizes a limited amount of a priori system knowledge. It is shown that a large class of non-linear systems can be modelled in this way, and indicated how to decompose the systems range of operation into operating regimes. Standard system identification algorithms can be used to identify the NARMAX model, and several aspects of the system identification problem are discussed and illustrated by a simulation example.Keywords
This publication has 16 references indexed in Scilit:
- Construction of composite models from observed dataInternational Journal of Control, 1992
- Multivariate Adaptive Regression SplinesThe Annals of Statistics, 1991
- Practical identification of NARMAX models using radial basis functionsInternational Journal of Control, 1990
- Neural networks for self-learning control systemsIEEE Control Systems Magazine, 1990
- Non-linear system identification using neural networksInternational Journal of Control, 1990
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989
- A prediction-error and stepwise-regression estimation algorithm for non-linear systemsInternational Journal of Control, 1986
- Input-output parametric models for non-linear systems Part I: deterministic non-linear systemsInternational Journal of Control, 1985
- Implementation of self-tuning regulators with variable forgetting factorsAutomatica, 1981
- Fitting autoregressive models for predictionAnnals of the Institute of Statistical Mathematics, 1969