Performance study of LMS based adaptive algorithms for unknown system identification

• Published in: AIP conference proceedings Vol. 1605; no. 1
• Main Authors: Javed, Shazia, Ahmad, Noor Atinah
• Format: Conference Proceeding Journal Article
• Language: English
• Published: Melville American Institute of Physics 10.07.2014
• Subjects: Adaptive algorithms; Adaptive filters; ADAPTIVE SYSTEMS; ADIABATIC SURFACE IONIZATION; ALGORITHMS; CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; COMPARATIVE EVALUATIONS; Comparative studies; Computer simulation; Convergence; Filtration; ITERATIVE METHODS; Misalignment; NOISE; Performance evaluation; Robustness; SENSITIVITY; SIGNALS; SIMULATION; Spectral sensitivity; STOCHASTIC PROCESSES; System identification
• ISSN: 0094-243X; 1935-0465; 1551-7616; 1551-7616
• DOI: 10.1063/1.4887594

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.