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
Kertas kerja ini membincangkan aplikasi rangkaian neural kabur-ARTMAP kepada penyamaan saluran komunikasi digital. Pendekatan ini menyediakan penyelesaian baharu untuk menyelesaikan masalah, seperti kerumitan dan latihan yang panjang, yang ditemui semasa melaksanakan penyamaan asas saraf yang dibangunkan sebelum ini. Penyamaan fuzzy-ARTMAP yang dicadangkan adalah pantas dan mudah untuk dilatih dan termasuk keupayaan yang tidak terdapat dalam pendekatan rangkaian saraf lain; sebilangan kecil parameter, tiada keperluan untuk pilihan pemberat awal, peningkatan automatik unit tersembunyi, tiada risiko terperangkap dalam minima tempatan, dan keupayaan menambah data baharu tanpa melatih semula data yang dilatih sebelum ini. Dalam kajian simulasi, isyarat binari dijana secara rawak dalam saluran linear dengan bunyi Gaussian. Prestasi penyamaan yang dicadangkan dibandingkan dengan penyamaan asas bersih saraf lain, khususnya penyamaan MLP dan RBF.
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Salinan
Jungsik LEE, Yeonsung CHOI, Jaewan LEE, Soowhan HAN, "Channel Equalization Using Fuzzy-ARTMAP" in IEICE TRANSACTIONS on Communications,
vol. E85-B, no. 4, pp. 826-830, April 2002, doi: .
Abstract: This paper discusses the application of a fuzzy-ARTMAP neural network to digital communications channel equalization. This approach provides new solutions for solving the problems, such as complexity and long training, which found when implementing the previously developed neural-basis equalizers. The proposed fuzzy-ARTMAP equalizer is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capability of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of the proposed equalizer is compared with other neural net basis equalizers, specifically MLP and RBF equalizers.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e85-b_4_826/_p
Salinan
@ARTICLE{e85-b_4_826,
author={Jungsik LEE, Yeonsung CHOI, Jaewan LEE, Soowhan HAN, },
journal={IEICE TRANSACTIONS on Communications},
title={Channel Equalization Using Fuzzy-ARTMAP},
year={2002},
volume={E85-B},
number={4},
pages={826-830},
abstract={This paper discusses the application of a fuzzy-ARTMAP neural network to digital communications channel equalization. This approach provides new solutions for solving the problems, such as complexity and long training, which found when implementing the previously developed neural-basis equalizers. The proposed fuzzy-ARTMAP equalizer is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capability of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of the proposed equalizer is compared with other neural net basis equalizers, specifically MLP and RBF equalizers.},
keywords={},
doi={},
ISSN={},
month={April},}
Salinan
TY - JOUR
TI - Channel Equalization Using Fuzzy-ARTMAP
T2 - IEICE TRANSACTIONS on Communications
SP - 826
EP - 830
AU - Jungsik LEE
AU - Yeonsung CHOI
AU - Jaewan LEE
AU - Soowhan HAN
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E85-B
IS - 4
JA - IEICE TRANSACTIONS on Communications
Y1 - April 2002
AB - This paper discusses the application of a fuzzy-ARTMAP neural network to digital communications channel equalization. This approach provides new solutions for solving the problems, such as complexity and long training, which found when implementing the previously developed neural-basis equalizers. The proposed fuzzy-ARTMAP equalizer is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capability of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of the proposed equalizer is compared with other neural net basis equalizers, specifically MLP and RBF equalizers.
ER -