System Identification using Orthonormal Basis Filters

D.T., Lemma and M., Ramasamy and M., Shuhaimi (2010) System Identification using Orthonormal Basis Filters. [Citation Index Journal]

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



The widely used dynamic models for identification of linear time invariant systems in process industries are Auto Regressive with Exogenous Input (ARX) and Finite Impulse Response (FIR) models. Their popularity is due to their simplicity in developing the model. However, they need very large amount of data to reduce variance error, in addition ordinary ARX model structures lead to inconsistent model parameters. Orthonormal Basis Filter (OBF) model structures permit incorporation of prior knowledge of the system in the form of one or more poles, which renders it the capacity to capture the system dynamics with a few number of parameters (parsimonious in parameters). In addition, the resulting OBF models are consistent in parameters. The model parameters can be easily developed using linear least square method. In this study, OBF model development for simulation and real case studies is presented.

Item Type:Citation Index Journal
Impact Factor:Nil
Uncontrolled Keywords:System identification Orthonomral basis filter ARX model FIR model
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Departments / MOR / COE:Departments > Chemical Engineering
ID Code:4511
Deposited By: Assoc Prof Dr Marappagounder Ramasamy
Deposited On:14 Mar 2011 03:10
Last Modified:19 Jan 2017 08:24

Repository Staff Only: item control page

Document Downloads

More statistics for this item...