The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Penapis digital penyesuaian LMS menggunakan aritmetik teragih (DA-ADF) telah dicadangkan. Cowan dan lain-lain mencadangkan algoritma penyesuaian DA dengan pengekodan binari mengimbangi untuk terbitan mudah algoritma dan penggunaan sifat simetri ganjil bagi ruang fungsi penyesuaian (AFS). Walau bagaimanapun, kami menunjukkan bahawa kelajuan penumpuan algoritma penyesuaian DA ini sangat terdegradasi oleh simulasi komputer kami. Untuk mengatasi masalah ini, kami telah mencadangkan algoritma penyesuaian DA yang digeneralisasikan dengan perwakilan pelengkap dua dan seni bina yang berkesan. DA-ADF kami mempunyai prestasi berkelajuan tinggi, kependaman keluaran kecil, kelajuan penumpuan yang baik, perkakasan berskala kecil dan pelesapan kuasa yang lebih rendah untuk pesanan yang lebih tinggi, secara serentak. Dalam makalah ini, kami menganalisis keadaan penumpuan algoritma penyesuaian DA yang tidak pernah dipertimbangkan secara teori. Daripada analisis ini, kami menunjukkan bahawa kelajuan penumpuan adalah bergantung pada taburan nilai eigen bagi matriks korelasi auto bagi vektor isyarat input lanjutan. Tambahan pula, kami memperoleh nilai eigen secara teori. Hasilnya, kami jelas menunjukkan bahawa DA-ADF kami mempunyai kelebihan DA-ADF konvensional dalam kelajuan penumpuan.
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Salinan
Kyo TAKAHASHI, Yoshitaka TSUNEKAWA, Norio TAYAMA, Kyoushirou SEKI, "Analysis of the Convergence Condition of LMS Adaptive Digital Filter Using Distributed Arithmetic" in IEICE TRANSACTIONS on Fundamentals,
vol. E85-A, no. 6, pp. 1249-1256, June 2002, doi: .
Abstract: An LMS adaptive digital filter using distributed arithmetic (DA-ADF) has been proposed. Cowan and others proposed the DA adaptive algorithm with offset binary coding for the simple derivation of an algorithm and the use of an odd-symmetry property of adaptive function space (AFS). However, we indicated that a convergence speed of this DA adaptive algorithm degraded extremely by our computer simulations. To overcome these problems, we have proposed the DA adaptive algorithm generalized with two's complement representation and effective architectures. Our DA-ADF has performances of a high speed, small output latency, a good convergence speed, small-scale hardware and lower power dissipation for higher order, simultaneously. In this paper, we analyze a convergence condition of DA adaptive algorithm that has never been considered theoretically. From this analysis, we indicate that the convergence speed is depended on a distribution of eigenvalues of an auto-correlation matrix of an extended input signal vector . Furthermore, we obtain the eigenvalues theoretically. As a result, we clearly show that our DA-ADF has an advantage of the conventional DA-ADF in the convergence speed.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e85-a_6_1249/_p
Salinan
@ARTICLE{e85-a_6_1249,
author={Kyo TAKAHASHI, Yoshitaka TSUNEKAWA, Norio TAYAMA, Kyoushirou SEKI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Analysis of the Convergence Condition of LMS Adaptive Digital Filter Using Distributed Arithmetic},
year={2002},
volume={E85-A},
number={6},
pages={1249-1256},
abstract={An LMS adaptive digital filter using distributed arithmetic (DA-ADF) has been proposed. Cowan and others proposed the DA adaptive algorithm with offset binary coding for the simple derivation of an algorithm and the use of an odd-symmetry property of adaptive function space (AFS). However, we indicated that a convergence speed of this DA adaptive algorithm degraded extremely by our computer simulations. To overcome these problems, we have proposed the DA adaptive algorithm generalized with two's complement representation and effective architectures. Our DA-ADF has performances of a high speed, small output latency, a good convergence speed, small-scale hardware and lower power dissipation for higher order, simultaneously. In this paper, we analyze a convergence condition of DA adaptive algorithm that has never been considered theoretically. From this analysis, we indicate that the convergence speed is depended on a distribution of eigenvalues of an auto-correlation matrix of an extended input signal vector . Furthermore, we obtain the eigenvalues theoretically. As a result, we clearly show that our DA-ADF has an advantage of the conventional DA-ADF in the convergence speed.},
keywords={},
doi={},
ISSN={},
month={June},}
Salinan
TY - JOUR
TI - Analysis of the Convergence Condition of LMS Adaptive Digital Filter Using Distributed Arithmetic
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1249
EP - 1256
AU - Kyo TAKAHASHI
AU - Yoshitaka TSUNEKAWA
AU - Norio TAYAMA
AU - Kyoushirou SEKI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E85-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2002
AB - An LMS adaptive digital filter using distributed arithmetic (DA-ADF) has been proposed. Cowan and others proposed the DA adaptive algorithm with offset binary coding for the simple derivation of an algorithm and the use of an odd-symmetry property of adaptive function space (AFS). However, we indicated that a convergence speed of this DA adaptive algorithm degraded extremely by our computer simulations. To overcome these problems, we have proposed the DA adaptive algorithm generalized with two's complement representation and effective architectures. Our DA-ADF has performances of a high speed, small output latency, a good convergence speed, small-scale hardware and lower power dissipation for higher order, simultaneously. In this paper, we analyze a convergence condition of DA adaptive algorithm that has never been considered theoretically. From this analysis, we indicate that the convergence speed is depended on a distribution of eigenvalues of an auto-correlation matrix of an extended input signal vector . Furthermore, we obtain the eigenvalues theoretically. As a result, we clearly show that our DA-ADF has an advantage of the conventional DA-ADF in the convergence speed.
ER -