TY  - JOUR
N2  - 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. 2010 Asian Network for Scientific Information.
VL  - 10
JF  - Journal of Applied Sciences
AV  - none
EP  - 2522
KW  - Bandpass filters; FIR filters; Identification (control systems); Impulse response; Invariance; Linear systems; Model structures; Religious buildings; Time varying control systems
KW  -  ARX model; Finite-impulse response; FIR model; Linear least square methods; Linear time invariant systems; Model development; Orthonormal basis; Scientific information
KW  -  Least squares approximations
PB  - Asian Network for Scientific Information
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049293818&doi=10.3923%2fjas.2010.2516.2522&partnerID=40&md5=3e028bf4e2969ff946d345e5fc036762
TI  - System Identification using Orthonormal Basis Filters
SN  - 18125654
ID  - scholars1381
A1  - Lemma, D.T.
A1  - Ramasamy, M.
A1  - Shuhaimi, M.
Y1  - 2010///
N1  - cited By 11
SP  - 2516
IS  - 21
ER  -