Improved Method for Development of Parsimonious Orthonormal Basis Filter Models

Lemma D, Tufa and M., Ramasamy and M., Shuhaimi (2011) Improved Method for Development of Parsimonious Orthonormal Basis Filter Models. [Citation Index Journal]

[img] PDF - Published Version
Restricted to Registered users only


Official URL: http://www.sciencedirect.com/science/journal/09591...


One of the major advantages of orthonormal basis filter (OBF) models is that they are parsimonious in parameters. However, this is true only if appropriate type of filter and reasonably accurate dominant poles of the system are used in developing the model. An arbitrary choice of filter type and poles may lead to non-parsimonious model. While the selection of the type of filter may be simple if the damping characteristics of the system are known, finding good estimates of the dominant pole(s) of the system is not a trivial task. Another important advantage of OBF model is the fact that time delays can be easily estimated and incorporated into the model. Currently, time delay of the system is estimated from the step response of the OBF model using the tangent method. While this method is effective in estimating the time delay of systems that can be accurately modeled by first order plus time delay (FOPTD) models, the accuracy is low for systems with second- and higher-order dynamics. In this paper, a scheme is proposed that will result in parsimonious OBF model and a better estimate of time delay starting from an arbitrary set of poles.

Item Type:Citation Index Journal
Impact Factor:2.235
Uncontrolled Keywords:Orthonormal basis filter System identification Simulation models Prediction models Multi step ahead prediction
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Departments / MOR / COE:Departments > Chemical Engineering
ID Code:4505
Deposited By: Assoc Prof Dr Marappagounder Ramasamy
Deposited On:14 Mar 2011 01:40
Last Modified:19 Jan 2017 08:23

Repository Staff Only: item control page

Document Downloads

More statistics for this item...