By Topic

Stationary linear and nonlinear system identification and predictor set completeness

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
P. Caines ; Harvard University, Cambridge, MA, USA

A general consistency theorem for stationary nonlinear prediction error estimators is presented. Since this theorem does not require the existence of a parameterized system generating the observations, it applies to the practical problem of modeling complex systems with simple parameterized models. In order to measure the quality of fit between a set of observed processes and a given candidate set of predictors, the notion of predictor set completeness is introduced. Several examples are given to illustrate this idea; in particular, a negative result concerning the completehess of certain sets of linear predictors is presented. The relationship of Ljung's definitions of identifiability to various notions of predictor set completeness is examined, and the strong consistency of maximum likelihood estimators for Gaussian autoregressive moving average systems is obtained via an application of our techniques. Finally, problems for future research are described.

Published in:

IEEE Transactions on Automatic Control  (Volume:23 ,  Issue: 4 )