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Adaptive Filter Coefficient
The Experts below are selected from a list of 72 Experts worldwide ranked by ideXlab platform
S.m. Ghosh – 1st expert on this subject based on the ideXlab platform

Generation of multidimensional variable step size sequential Adaptive gradient algorithms with identification and noise cancellation applications
1991. IEEE International Sympoisum on Circuits and Systems, 1991CoAuthors: W.b. Mikhael, S.m. GhoshAbstract:The development of twodimensional, gradientbased sequential algorithms with applications in 2D system identification and noise cancellation is addressed. Existing gradient sequential algorithms use a convergence factor which is used to adjust the twodimensional Adaptive Filter Coefficients at each iteration. The performance of the algorithm depends entirely on the accuracy of the estimated convergence factor. The objective of the present work is to derive the optimality criterion governing the choice of the convergence factor in the case of 2D gradientbased sequential algorithms. The 2D variable stepsize sequential algorithms meeting the above constraint are proposed and investigated: the 2D individual Adaptive (TDIA) and the 2D homogeneous Adaptive (TDHA) algorithms. The TDIA algorithm uses optimal convergence factors tailored for each 2D Adaptive Filter Coefficient at each iteration. The TDHA algorithm uses the same convergence factor for all Coefficients but is optimally updated at each iteration.

Twodimensional variable stepsize sequential Adaptive gradient algorithms with applications
IEEE Transactions on Circuits and Systems, 1991CoAuthors: W.b. Mikhael, S.m. GhoshAbstract:The optimality criterion governing the choice of the convergence factor for the 2D sequential Adaptive gradient algorithms is developed. Two 2D variable stepsize sequential algorithms satisfying the proposed optimality constraint are derived and investigated. These are the 2D individual adaptation (TDIA) algorithm and the 2D homogeneous adaptation (TDHA) algorithm. The TDIA algorithm uses 2D optimal convergence factors tailored for each 2D Adaptive Filter Coefficient at each iteration. The TDHA algorithm uses the same convergence factor for all the Filter Coefficients, but the convergence factor is optimally updated at each iteration. Neither algorithm requires any a priori knowledge about the statistics of the system signals. In addition, the convergence factors are easily obtained from readily available signals without any differentiation or matrix inversions. The convergence characteristics and adaptation accuracy are greatly improved at the expense of a modest increase in computational complexity.
Dennis R. Morgan – 2nd expert on this subject based on the ideXlab platform

A PNLMS Algorithm With Individual Activation Factors
IEEE Transactions on Signal Processing, 2010CoAuthors: Francisco Das Chagas De Souza, Orlando José Tobias, Rui Seara, Dennis R. MorganAbstract:This paper presents a proportionate normalized leastmeansquare (PNLMS) algorithm using individual activation factors for each Adaptive Filter Coefficient, instead of a global activation factor as in the standard PNLMS algorithm. The proposed individual activation factors, determined in terms of the corresponding Adaptive Filter Coefficients, are recursively updated. This approach leads to a better distribution of the adaptation energy over the Filter Coefficients than the standard PNLMS does. Thereby, for impulse responses exhibiting high sparseness, the proposed algorithm achieves faster convergence, outperforming both the PNLMS and improved PNLMS (IPNLMS) algorithms.

EUSIPCO – Alternative approach for computing the activation factor of the PNLMS algorithm
, 2009CoAuthors: Francisco Das Chagas De Souza, Orlando José Tobias, Rui Seara, Dennis R. MorganAbstract:This paper presents a proportionate normalized leastmeansquare (PNLMS) algorithm using an individual activation factor for each Adaptive Filter Coefficient. Such strategy is used instead of a global activation factor as in the standard PNLMS algorithm. The proposed individual activation factors, determined in terms of the corresponding Adaptive Filter Coefficients, lead to a better distribution of the adaptation energy over the Filter Coefficients than the standard PNLMS does. Thereby, for impulse responses exhibiting high sparseness, the proposed algorithm achieves faster convergence, outperforming both the PNLMS and improved PNLMS (IPNLMS) algorithms.

Alternative approach for computing the activation factor of the PNLMS algorithm
2009 17th European Signal Processing Conference, 2009CoAuthors: Francisco Das C. De Souza, Rui Seara, Orlando José Tobias, Dennis R. MorganAbstract:This paper presents a proportionate normalized leastmeansquare (PNLMS) algorithm using an individual activation factor for each Adaptive Filter Coefficient. Such strategy is used instead of a global activation factor as in the standard PNLMS algorithm. The proposed individual activation factors, determined in terms of the corresponding Adaptive Filter Coefficients, lead to a better distribution of the adaptation energy over the Filter Coefficients than the standard PNLMS does. Thereby, for impulse responses exhibiting high sparseness, the proposed algorithm achieves faster convergence, outperforming both the PNLMS and improved PNLMS (IPNLMS) algorithms.
W.b. Mikhael – 3rd expert on this subject based on the ideXlab platform

Generation of multidimensional variable step size sequential Adaptive gradient algorithms with identification and noise cancellation applications
1991. IEEE International Sympoisum on Circuits and Systems, 1991CoAuthors: W.b. Mikhael, S.m. GhoshAbstract:The development of twodimensional, gradientbased sequential algorithms with applications in 2D system identification and noise cancellation is addressed. Existing gradient sequential algorithms use a convergence factor which is used to adjust the twodimensional Adaptive Filter Coefficients at each iteration. The performance of the algorithm depends entirely on the accuracy of the estimated convergence factor. The objective of the present work is to derive the optimality criterion governing the choice of the convergence factor in the case of 2D gradientbased sequential algorithms. The 2D variable stepsize sequential algorithms meeting the above constraint are proposed and investigated: the 2D individual Adaptive (TDIA) and the 2D homogeneous Adaptive (TDHA) algorithms. The TDIA algorithm uses optimal convergence factors tailored for each 2D Adaptive Filter Coefficient at each iteration. The TDHA algorithm uses the same convergence factor for all Coefficients but is optimally updated at each iteration.

Twodimensional variable stepsize sequential Adaptive gradient algorithms with applications
IEEE Transactions on Circuits and Systems, 1991CoAuthors: W.b. Mikhael, S.m. GhoshAbstract:The optimality criterion governing the choice of the convergence factor for the 2D sequential Adaptive gradient algorithms is developed. Two 2D variable stepsize sequential algorithms satisfying the proposed optimality constraint are derived and investigated. These are the 2D individual adaptation (TDIA) algorithm and the 2D homogeneous adaptation (TDHA) algorithm. The TDIA algorithm uses 2D optimal convergence factors tailored for each 2D Adaptive Filter Coefficient at each iteration. The TDHA algorithm uses the same convergence factor for all the Filter Coefficients, but the convergence factor is optimally updated at each iteration. Neither algorithm requires any a priori knowledge about the statistics of the system signals. In addition, the convergence factors are easily obtained from readily available signals without any differentiation or matrix inversions. The convergence characteristics and adaptation accuracy are greatly improved at the expense of a modest increase in computational complexity.