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 adaptif linear digunakan pada pelbagai bidang untuk kesederhanaan mereka dalam reka bentuk dan pelaksanaan. Memandangkan banyak jenis bukan linear yang wujud dalam sistem praktikal, bagaimanapun, penapisan penyesuaian bukan linear akan lebih diingini. Kertas kerja ini membentangkan kaedah reka bentuk untuk penapis digital penyesuaian bukan linear keluaran tunggal berbilang input menggunakan rangkaian saraf berulang. Tambahan pula, jika dibandingkan dengan kaedah ini dan kaedah berasaskan teori linear konvensional, jika kaedah yang dicadangkan digunakan, keputusan yang lebih baik boleh diperolehi, dan, kemungkinan kecekapan pembelajaran dapat ditingkatkan, kerana pembelajaran selari dijalankan dalam kaedah ini. Akhir sekali, hasil simulasi komputer dibentangkan untuk menggambarkan keberkesanan kaedah yang dicadangkan.
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Salinan
Jianming LU, Hua LIN, Xiaoqiu WANG, Takashi YAHAGI, "Multi-Input Single-Output Nonlinear Adaptive Digital Filters Using Recurrent Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 8, pp. 1942-1950, August 2001, doi: .
Abstract: Linear adaptive digital filters are applied to various fields for their simplicity in the design and implementation. Considering many kinds of nonlinearities inherent in practical systems, however, nonlinear adaptive filtering will be more desirable. This paper presents a design method for multi-input single-output nonlinear adaptive digital filters using recurrent neural networks. Furthermore, in comparison with this method and the method based on the conventional linear theory, if the proposed method is used, better results can be obtained, and, it is possible that the learning efficiency is improved, because the parallel learning is carried out in this method. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_8_1942/_p
Salinan
@ARTICLE{e84-a_8_1942,
author={Jianming LU, Hua LIN, Xiaoqiu WANG, Takashi YAHAGI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multi-Input Single-Output Nonlinear Adaptive Digital Filters Using Recurrent Neural Networks},
year={2001},
volume={E84-A},
number={8},
pages={1942-1950},
abstract={Linear adaptive digital filters are applied to various fields for their simplicity in the design and implementation. Considering many kinds of nonlinearities inherent in practical systems, however, nonlinear adaptive filtering will be more desirable. This paper presents a design method for multi-input single-output nonlinear adaptive digital filters using recurrent neural networks. Furthermore, in comparison with this method and the method based on the conventional linear theory, if the proposed method is used, better results can be obtained, and, it is possible that the learning efficiency is improved, because the parallel learning is carried out in this method. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.},
keywords={},
doi={},
ISSN={},
month={August},}
Salinan
TY - JOUR
TI - Multi-Input Single-Output Nonlinear Adaptive Digital Filters Using Recurrent Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1942
EP - 1950
AU - Jianming LU
AU - Hua LIN
AU - Xiaoqiu WANG
AU - Takashi YAHAGI
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2001
AB - Linear adaptive digital filters are applied to various fields for their simplicity in the design and implementation. Considering many kinds of nonlinearities inherent in practical systems, however, nonlinear adaptive filtering will be more desirable. This paper presents a design method for multi-input single-output nonlinear adaptive digital filters using recurrent neural networks. Furthermore, in comparison with this method and the method based on the conventional linear theory, if the proposed method is used, better results can be obtained, and, it is possible that the learning efficiency is improved, because the parallel learning is carried out in this method. Finally, the results of computer simulation are presented to illustrate the effectiveness of the proposed method.
ER -