System Identification using Adaptive Filters
Syed Saalim1, Anush M2, Arpitha V3, Sudheesh K V4
1Syed Saalim, Department of ECE, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
2Anush M, Department of ECE, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
3Arpitha V, Department of ECE, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
4Sudheesh K V, Assistant Professor, Department of ECE, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
Manuscript received on June 12, 2020. | Revised Manuscript received on June 24, 2020. | Manuscript published on July 10, 2020. | PP: 409-414 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I7077079920 | DOI: 10.35940/ijitee.I7077.079920
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: System Identification, Adaptive filter, Least Mean Square algorithm, NLMS algorithm, RLS algorithm.
Scope of the Article: Algorithm Engineering