NARMAX model identification using a randomised approach
by Pedro Felipe Leite Retes; Luis Antonio Aguirre
International Journal of Modelling, Identification and Control (IJMIC), Vol. 31, No. 3, 2019

Abstract: Structure selection is one of the most critical steps in nonlinear system identification. A large family of methods, based on model prediction error, use concepts and tools from linear algebra. Other methods, based on model simulation error, have to deal with non-convex optimisation problems. More recently a family of methods have been put forward that has probabilistic setting. The randomised algorithm for model structure selection (RaMSS) belongs to this family and it has been shown to be effective to select regressors for NARX models. In the present paper, such a method is extended to cope with NARMAX models. The performance of the proposed method is illustrated using simulated and experimental data. It is shown that the proposed method is capable of correctly selecting model structures from simulation data. The method was also applied to experimental data with successful results.

Online publication date: Tue, 02-Apr-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com