title: A stochastic total least squares solution of adaptive filtering problem.
abstract: An efficient and computationally linear algorithm is derived for total least squares solution of adaptive filtering problem, when both input and output signals are contaminated by noise. The proposed total least mean squares (TLMS) algorithm is designed by recursively computing an optimal solution of adaptive TLS problem by minimizing instantaneous value of weighted cost function. Convergence analysis of the algorithm is given to show the global convergence of the proposed algorithm, provided that the stepsize parameter is appropriately chosen. The TLMS algorithm is computationally simpler than the other TLS algorithms and demonstrates a better performance as compared with the least mean square (LMS) and normalized least mean square (NLMS) algorithms. It provides minimum mean square deviation by exhibiting better convergence in misalignment for unknown system identification under noisy inputs.
authors.affiliation: School of Mathematical Science, Universiti Sains Malaysia, 11800 Penang, Malaysia ; Lahore College for Women University, Lahore 54000, Pakistan.
authors.forename: Shazia
authors.identifier: 0000-0001-5075-5557
authors.initials: S
authors.lastname: Javed
authors.affiliation: School of Mathematical Science, Universiti Sains Malaysia, 11800 Penang, Malaysia.
authors.forename: Noor Atinah
authors.initials: NA
authors.lastname: Ahmad
mesh_terms: D016018:Least-Squares Analysis; D013269:Stochastic Processes