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
ITU-R 데이터 뱅크의 전파, 기상 및 지리 데이터를 기반으로 강우 감쇠 예측을 위한 인공 신경망의 적합성에 대한 조사가 제시됩니다. 측정을 통한 적응형 학습을 기반으로 무선 통신을 위한 강우 감쇠 예측 모델을 향한 첫 번째 성공적인 단계가 이루어졌습니다. 인공신경망 기반 모델을 이용한 강우감쇠 예측은 측정값과 좋은 일치성을 보였다. 또한, 새로운 진화 시스템인 EPNet을 사용하여 구조와 중량 모두에서 얻은 인공 신경망 강우 감쇠 모델을 진화시켰으며, EPNet으로 진화된 인공 신경망을 기반으로 더 간단한 구조와 더 나은 예측 정확도를 갖춘 최적의 강우 감쇠 모델을 얻습니다. ITU-R 모델과 비교하여 본 논문에서 제안한 EPNet 진화형 강우 감쇠 인공 신경망 모델은 강우 감쇠 예측의 정확도를 향상시키고 강우 감쇠를 예측하는 새로운 방법을 제시합니다.
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부
Hongwei YANG, Chen HE, Hongwen ZHU, Wentao SONG, "Earth-Space Rain Attenuation Model Based on EPNet-Evolved Artificial Neural Network" in IEICE TRANSACTIONS on Communications,
vol. E84-B, no. 9, pp. 2540-2549, September 2001, doi: .
Abstract: Investigations into the suitability of artificial neural network for the prediction of rain attenuation based on radio, meteorological and geographical data from ITU-R data bank are presented. First successful steps towards a prediction model of rain attenuation for radio communication based on adaptive learning from the measurement are made. Rain attenuation prediction with the model based on artificial neural network shows good conformity with the measurement. Moreover, a new evolutionary system, EPNet is used to evolve the artificial neural network rain attenuation model obtained both in architecture and weight, and an optimal rain attenuation model with simpler architecture and better prediction accuracy based on EPNet-evolved artificial neural network is obtained. Compared with the ITU-R model, the EPNet-evolved artificial neural network model of rain attenuation proposed in this paper improves the accuracy of rain attenuation prediction and creates a novel way to predict rain attenuation.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e84-b_9_2540/_p
부
@ARTICLE{e84-b_9_2540,
author={Hongwei YANG, Chen HE, Hongwen ZHU, Wentao SONG, },
journal={IEICE TRANSACTIONS on Communications},
title={Earth-Space Rain Attenuation Model Based on EPNet-Evolved Artificial Neural Network},
year={2001},
volume={E84-B},
number={9},
pages={2540-2549},
abstract={Investigations into the suitability of artificial neural network for the prediction of rain attenuation based on radio, meteorological and geographical data from ITU-R data bank are presented. First successful steps towards a prediction model of rain attenuation for radio communication based on adaptive learning from the measurement are made. Rain attenuation prediction with the model based on artificial neural network shows good conformity with the measurement. Moreover, a new evolutionary system, EPNet is used to evolve the artificial neural network rain attenuation model obtained both in architecture and weight, and an optimal rain attenuation model with simpler architecture and better prediction accuracy based on EPNet-evolved artificial neural network is obtained. Compared with the ITU-R model, the EPNet-evolved artificial neural network model of rain attenuation proposed in this paper improves the accuracy of rain attenuation prediction and creates a novel way to predict rain attenuation.},
keywords={},
doi={},
ISSN={},
month={September},}
부
TY - JOUR
TI - Earth-Space Rain Attenuation Model Based on EPNet-Evolved Artificial Neural Network
T2 - IEICE TRANSACTIONS on Communications
SP - 2540
EP - 2549
AU - Hongwei YANG
AU - Chen HE
AU - Hongwen ZHU
AU - Wentao SONG
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E84-B
IS - 9
JA - IEICE TRANSACTIONS on Communications
Y1 - September 2001
AB - Investigations into the suitability of artificial neural network for the prediction of rain attenuation based on radio, meteorological and geographical data from ITU-R data bank are presented. First successful steps towards a prediction model of rain attenuation for radio communication based on adaptive learning from the measurement are made. Rain attenuation prediction with the model based on artificial neural network shows good conformity with the measurement. Moreover, a new evolutionary system, EPNet is used to evolve the artificial neural network rain attenuation model obtained both in architecture and weight, and an optimal rain attenuation model with simpler architecture and better prediction accuracy based on EPNet-evolved artificial neural network is obtained. Compared with the ITU-R model, the EPNet-evolved artificial neural network model of rain attenuation proposed in this paper improves the accuracy of rain attenuation prediction and creates a novel way to predict rain attenuation.
ER -