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Adaptive Algorithm

The Experts below are selected from a list of 50049 Experts worldwide ranked by ideXlab platform

Xingmei Zhong – 1st expert on this subject based on the ideXlab platform

  • An Adaptive Algorithm for narrow-band interference rejection in DSSS systems
    2000 IEEE International Symposium on Circuits and Systems (ISCAS), 2000
    Co-Authors: Guowen Song, Dapeng Yu, Xingmei Zhong

    Abstract:

    In this paper, a fast convergent nonlinear Adaptive Algorithm for narrow-band interference rejection in direct sequence spread spectrum (DSSS) systems is proposed. Simulation results show that the new Algorithm can significantly increase the convergence rate of the nonlinear Adaptive filter.

Guowen Song – 2nd expert on this subject based on the ideXlab platform

  • An Adaptive Algorithm for narrow-band interference rejection in DSSS systems
    2000 IEEE International Symposium on Circuits and Systems (ISCAS), 2000
    Co-Authors: Guowen Song, Dapeng Yu, Xingmei Zhong

    Abstract:

    In this paper, a fast convergent nonlinear Adaptive Algorithm for narrow-band interference rejection in direct sequence spread spectrum (DSSS) systems is proposed. Simulation results show that the new Algorithm can significantly increase the convergence rate of the nonlinear Adaptive filter.

Muhammad Moinuddin – 3rd expert on this subject based on the ideXlab platform

  • A noise constrained least mean fourth (NCLMF) Adaptive Algorithm
    Signal Processing, 2020
    Co-Authors: Azzedine Zerguine, Muhammad Moinuddin, Syed Ali Aamir Imam

    Abstract:

    The learning speed of an Adaptive Algorithm can be improved by properly constraining the cost function of the Adaptive Algorithm. In this work, a noise-constrained least mean fourth (NCLMF) Adaptive Algorithm is proposed. The NCLMF Algorithm is obtained by constraining the cost function of the standard LMF Algorithm to the fourth-order moment of the additive noise. The NCLMF Algorithm can be seen as a variable step-size LMF Algorithm. The main aim of this work is to derive the NCLMF Adaptive Algorithm, analyze its convergence behavior, and assess its performance in different noise environments. Furthermore, the analysis of the proposed NCLMF Algorithm is carried out using the concept of energy conservation. Finally, a number of simulation results are carried out to corroborate the theoretical findings, and as expected, improved performance is obtained through the use of this technique over the traditional LMF Algorithm.

  • Convergence and tracking analysis of a constrained least mean fourth Adaptive Algorithm
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Syed Ali Aamir Imam, Azzedine Zerguine, Muhammad Moinuddin

    Abstract:

    It is a well established fact that the addition of a constraint to an Adaptive Algorithm improves its performance properties. Consequently, in this work, a noise-constrained least mean fourth (NCLMF) Adaptive Algorithm is developed. The NCLMF Algorithm is based on a constrained minimization problem that includes knowledge of the noise variance. Moreover, this noise constrained LMF Algorithm can be seen as a variable-step-size LMF Algorithm. The convergence analysis as well the tracking analysis of the NCLMF Adaptive Algorithm are developed using the concept of energy conservation. Finally, simulation results are presented to demonstrate the superiority of the NCLMF Algorithm over the conventional LMF Algorithm as well corroborating the theoretical findings.