Abstract
In developing model-based methods for state estimation or control of a priori unknown dynamic processes, the first step is to establish plant models from available observational data and/or expert process knowledge. Except for the usual requirement of the model approximation ability, it is also required that the model structure is well suited for applications in the consequent state estimation and control algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Harris, C., Hong, X., Gan, Q. (2002). Neurofuzzy linearisation modelling for nonlinear state estimation. In: Adaptive Modelling, Estimation and Fusion from Data. Advanced Information Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18242-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-18242-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-62119-2
Online ISBN: 978-3-642-18242-6
eBook Packages: Springer Book Archive