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工程科学学报 Chinese Journal of Engineering 卷积神经网络在矿区预测中的研究与应用 袁传新贾东宁周生辉 Research and application of convolutional neural network in mining area prediction YUAN Chuan-xin,JIA Dong-ning.ZHOU Sheng-hui 引用本文: 袁传新,贾东宁,周生辉.卷积神经网络在矿区预测中的研究与应用.工程科学学报,2020,42(12):1597-1604.doi: 10.13374j.issn2095-9389.2020.01.02.001 YUAN Chuan-xin,JIA Dong-ning,ZHOU Sheng-hui.Research and application of convolutional neural network in mining area prediction[J].Chinese Journal of Engineering,2020,42(12):1597-1604.doi:10.13374/j.issn2095-9389.2020.01.02.001 在线阅读View online::htps:/ldoi.org10.13374.issn2095-9389.2020.01.02.001 您可能感兴趣的其他文章 Articles you may be interested in 基于卷积神经网络的反无人机系统声音识别方法 Sound recognition method of an anti-UAV system based on a convolutional neural network 工程科学学报.2020.42(11):1516 https:/doi.org10.13374.issn2095-9389.2020.06.30.008 基于深度卷积神经网络的地磁导航方向适配性分析 Direction-matching-suitability analysis for geomagnetic navigation based on convolutional neural networks 工程科学学报.2017,3910:1584htps:ldoi.org10.13374.issn2095-9389.2017.10.018 基于集成神经网络的剩余寿命预测 Remaining useful life prediction based on an integrated neural network 工程科学学报.2020,42(10:1372htps:1doi.org/10.13374.issn2095-9389.2019.10.10.005 BP神经网络F钢铝耗的预测模型 Prediction model of aluminum consumption with BP neural networks in IF steel production 工程科学学报.2017,394:511 https:oi.org10.13374j.issn2095-9389.2017.04.005 深度神经网络模型压缩综述 A survey of model compression for deep neural networks 工程科学学报.2019.41(10:1229 https:/doi.org10.13374.issn2095-9389.2019.03.27.002 基于BP神经网络的机器人波动摩擦力矩修正方法 Wave friction correction method for a robot based on BP neural network 工程科学学报.2019,41(8:1085htps:/1doi.org/10.13374issn2095-9389.2019.08.014卷积神经网络在矿区预测中的研究与应用 袁传新 贾东宁 周生辉 Research and application of convolutional neural network in mining area prediction YUAN Chuan-xin, JIA Dong-ning, ZHOU Sheng-hui 引用本文: 袁传新, 贾东宁, 周生辉. 卷积神经网络在矿区预测中的研究与应用[J]. 工程科学学报, 2020, 42(12): 1597-1604. doi: 10.13374/j.issn2095-9389.2020.01.02.001 YUAN Chuan-xin, JIA Dong-ning, ZHOU Sheng-hui. Research and application of convolutional neural network in mining area prediction[J]. Chinese Journal of Engineering, 2020, 42(12): 1597-1604. doi: 10.13374/j.issn2095-9389.2020.01.02.001 在线阅读 View online: https://doi.org/10.13374/j.issn2095-9389.2020.01.02.001 您可能感兴趣的其他文章 Articles you may be interested in 基于卷积神经网络的反无人机系统声音识别方法 Sound recognition method of an anti-UAV system based on a convolutional neural network 工程科学学报. 2020, 42(11): 1516 https://doi.org/10.13374/j.issn2095-9389.2020.06.30.008 基于深度卷积神经网络的地磁导航方向适配性分析 Direction-matching-suitability analysis for geomagnetic navigation based on convolutional neural networks 工程科学学报. 2017, 39(10): 1584 https://doi.org/10.13374/j.issn2095-9389.2017.10.018 基于集成神经网络的剩余寿命预测 Remaining useful life prediction based on an integrated neural network 工程科学学报. 2020, 42(10): 1372 https://doi.org/10.13374/j.issn2095-9389.2019.10.10.005 BP神经网络IF钢铝耗的预测模型 Prediction model of aluminum consumption with BP neural networks in IF steel production 工程科学学报. 2017, 39(4): 511 https://doi.org/10.13374/j.issn2095-9389.2017.04.005 深度神经网络模型压缩综述 A survey of model compression for deep neural networks 工程科学学报. 2019, 41(10): 1229 https://doi.org/10.13374/j.issn2095-9389.2019.03.27.002 基于BP神经网络的机器人波动摩擦力矩修正方法 Wave friction correction method for a robot based on BP neural network 工程科学学报. 2019, 41(8): 1085 https://doi.org/10.13374/j.issn2095-9389.2019.08.014
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