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Author: search.filters.author.Loetwassana, W.Subject: search.filters.subject.Gradient algorithm

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    Unbiased plain gradient algorithm for a second-order adaptive IIR notch filter with constrained poles and zeros
    (2010-08-01)
    Loetwassana, W.
    ;
    Punchalard, R.
    ;
    Koseeyaporn, J.
    ;
    Wardkein, P.
    This article proposes an unbiased plain gradient algorithm for a second-order adaptive IIR notch filter with constrained poles and zeros. The proposed algorithm employs removing a dominant parameter that produces inherent bias. By using this technique, the performances are improved with slight expense in computational complexity. In this paper, theoretical analysis for deriving the estimations of bias and mean square error (MSE) at steady state are presented in closed form. Moreover, the stability bound of the algorithm is also derived. To confirm the analytical results, the computer simulations are provided to corroborate the effectiveness of the proposed algorithm. Furthermore, the performances of the algorithm are also compared with the plain gradient (PG) and modified plain gradient (MPG) algorithms. © 2010 Elsevier B.V. All rights reserved.
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    Direct frequency estimation based adaptive algorithm for a second-order adaptive FIR notch filter
    (2008-02-01)
    Punchalard, R.
    ;
    Lorsawatsiri, A.
    ;
    Loetwassana, W.
    ;
    Koseeyaporn, J.
    ;
    Wardkein, P.
    This work deals with the problem of the frequency estimation of a sinusoidal signal corrupted by broad-band noise. The direct frequency estimation based adaptive algorithm for a second-order adaptive finite impulse response (FIR) notch filter (AFNF) is thus proposed. The proposed algorithm employs the bias removal technique to remove the bias existing in the estimated parameter. The performances including the rate of convergence and the mean square error (MSE) can be easily controlled by using only one parameter, i.e., step size parameter. Moreover, the proposed filter is simple to implement and suitable for real-time applications. In addition, the difference equations for the convergence in the mean and mean square, and the closed form expressions for the steady-state estimation bias and MSE are also carried out. Finally, the simulation results are provided to confirm the theoretical analysis. © 2007 Elsevier B.V. All rights reserved.