Overestimation Trap of Artificial Neural Network: Learning the Rule of PRBS | IEEE Conference Publication | IEEE Xplore
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Overestimation Trap of Artificial Neural Network: Learning the Rule of PRBS

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Liang Shu; Jianqiang Li; Zhiquan Wan; Wenjia Zhang; Songnian Fu; Kun Xu
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Abstract:

We investigate the overestimation trap of ANN from learning the rule of PRBS over unrepeated pattern. The influence of input symbols and hidden neurons is studied by simu...Show More

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Abstract:

We investigate the overestimation trap of ANN from learning the rule of PRBS over unrepeated pattern. The influence of input symbols and hidden neurons is studied by simulations. Besides, the overestimated gain is experimentally evaluated.
Published in: 2018 European Conference on Optical Communication (ECOC)
Date of Conference: 23-27 September 2018
Date Added to IEEE Xplore: 15 November 2018
ISBN Information:
DOI: 10.1109/ECOC.2018.8535327
Conference Location: Rome, Italy
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