Open AccessSystem Identification using Adaptive Filters
Author(s) -
Syed Saalim*,
M Anush,
V Arpitha,
K V Sudheesh
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i7077.079920
Subject(s) - adaptive filter , control theory (sociology) , kernel adaptive filter , infinite impulse response , identification (biology) , filter (signal processing) , system identification , computer science , filter design , finite impulse response , least mean squares filter , prototype filter , adaptive system , mathematics , digital filter , algorithm , artificial intelligence , botany , control (management) , database , computer vision , biology , measure (data warehouse)
Identification of system is one of the major applications of an adaptive filters, mainly Least Mean Square (LMS) algorithm, because of its ease in calculations, the ability to withstand or overcome any conditions. The unknown System can be a FIR or an IIR filter. Same input is fed into both undefined system (which is unknown to us) and the adaptive filter, their outputs will be subtracted and the error subtracted signal will be given to adaptive filter. The adaptive filter is modified until the system which is unknown and the adaptive filter becomes relatively equal. System identification defines the type and functionality of the system. By utilizing the weights, the output of the system for any input can be predicted.