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
Algoritma kuasa dua terkecil (LMS) telah digunakan secara meluas untuk penapisan adaptif kerana mudah dilaksanakan pada kerumitan pengiraan O(2N) di mana N ialah bilangan pili. Kelemahan algoritma LMS ialah prestasinya sensitif terhadap penskalaan input. Algoritma LMS ternormal (NLMS) menyelesaikan masalah ini pada algoritma LMS dengan menormalkan dengan kuasa tetingkap gelongsor input; bagaimanapun, penormalan ini meningkatkan kos pengiraan kepada O(3N) setiap lelaran. Dalam kerja ini, kami memperoleh formula baharu untuk melaksanakan algoritma NLMS dengan ketat pada kerumitan pengiraan O(2N), yang dirujuk sebagai algoritma C-NLMS. Terbitan algoritma C-NLMS menggunakan H∞ rangka kerja yang dibentangkan sebelum ini oleh salah seorang pengarang untuk mencipta pandangan bersatu algoritma penapisan penyesuaian. Kesahan algoritma C-NLMS disahkan menggunakan simulasi.
Kiyoshi NISHIYAMA
Iwate University
Masahiro SUNOHARA
Rion Co., Ltd.
Nobuhiko HIRUMA
Rion Co., Ltd.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Kiyoshi NISHIYAMA, Masahiro SUNOHARA, Nobuhiko HIRUMA, "A New Formula to Compute the NLMS Algorithm at a Computational Complexity of O(2N)" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 11, pp. 1545-1549, November 2019, doi: 10.1587/transfun.E102.A.1545.
Abstract: The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of O(2N) where N is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to O(3N) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of O(2N), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H∞ framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1545/_p
Salinan
@ARTICLE{e102-a_11_1545,
author={Kiyoshi NISHIYAMA, Masahiro SUNOHARA, Nobuhiko HIRUMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A New Formula to Compute the NLMS Algorithm at a Computational Complexity of O(2N)},
year={2019},
volume={E102-A},
number={11},
pages={1545-1549},
abstract={The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of O(2N) where N is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to O(3N) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of O(2N), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H∞ framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.},
keywords={},
doi={10.1587/transfun.E102.A.1545},
ISSN={1745-1337},
month={November},}
Salinan
TY - JOUR
TI - A New Formula to Compute the NLMS Algorithm at a Computational Complexity of O(2N)
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1545
EP - 1549
AU - Kiyoshi NISHIYAMA
AU - Masahiro SUNOHARA
AU - Nobuhiko HIRUMA
PY - 2019
DO - 10.1587/transfun.E102.A.1545
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2019
AB - The least mean squares (LMS) algorithm has been widely used for adaptive filtering because of easily implementing at a computational complexity of O(2N) where N is the number of taps. The drawback of the LMS algorithm is that its performance is sensitive to the scaling of the input. The normalized LMS (NLMS) algorithm solves this problem on the LMS algorithm by normalizing with the sliding-window power of the input; however, this normalization increases the computational cost to O(3N) per iteration. In this work, we derive a new formula to strictly perform the NLMS algorithm at a computational complexity of O(2N), that is referred to as the C-NLMS algorithm. The derivation of the C-NLMS algorithm uses the H∞ framework presented previously by one of the authors for creating a unified view of adaptive filtering algorithms. The validity of the C-NLMS algorithm is verified using simulations.
ER -