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Title: Performance study of LMS based adaptive algorithms for unknown system identification

Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.
Authors:
;  [1]
  1. School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Penang (Malaysia)
Publication Date:
OSTI Identifier:
22306148
Resource Type:
Journal Article
Resource Relation:
Journal Name: AIP Conference Proceedings; Journal Volume: 1605; Journal Issue: 1; Conference: SKSM21: 21. national symposium on mathematical sciences: Germination of mathematical sciences education and research towards global sustainability, Penang (Malaysia), 6-8 Nov 2013; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ADAPTIVE SYSTEMS; ADIABATIC SURFACE IONIZATION; ALGORITHMS; COMPARATIVE EVALUATIONS; ITERATIVE METHODS; NOISE; PERFORMANCE; SENSITIVITY; SIGNALS; SIMULATION; STOCHASTIC PROCESSES