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浏览/检索结果: 共5条，第1-5条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Beyond the 'Code': A Guide to the Description and Documentation of Biodiversity in Ciliated Protists (Alveolata, Ciliophora) 期刊论文 2017, 卷号: 64, 期号: [db:dc_citation_issue], 页码: 539 作者:  Warren, Alan[1];  Patterson, David J.[2];  Dunthorn, Micah[3];  Clamp, John C.[4];  Achilles-Day, Undine E. M.[5] 收藏  |  浏览/下载：22/0  |  提交时间：2019/12/03 Menarche, menopause, and breast cancer risk: Individual participant meta-analysis, including 118 964 women with breast cancer from 117 epidemiological studies 期刊论文 The Lancet Oncology, 2012, 卷号: 13, 期号: 11 作者:  Hamajima, Nobuyuki/7102203082[0];  Hirose, Kaoru/35398958500[1];  Tajima, Kazuo/35374000300[2];  Rohan, Thomas E./7005205148[3];  Friedenreich, Christine M./7004933140[4] 收藏  |  浏览/下载：25/0  |  提交时间：2019/12/19 Optimal model on integrated production planning and scheduling of steelmaking and continuous casting process 期刊论文 2010, 卷号: 33, 页码: 108-113 作者:  Zheng, Zhong[1];  Liu, Hai-Yu[1];  Gao, Xiao-Qiang[2];  He, La-Mei[1];  Zhang, Tao[1] 收藏  |  浏览/下载：1/0  |  提交时间：2019/11/28 Cooperative reinforcement learning algorithm to distributed power system based on multi-agent (EI收录) 会议论文 2009 3rd International Conference on Power Electronics Systems and Applications, PESA 2009, Hong Kong, China, May 20, 2009 - May 22, 2009 作者:  Gao, La-Mei[1];  Zeng, Jun[1];  Wu, Jie[1];  Li, Min[1] 收藏  |  浏览/下载：3/0  |  提交时间：2019/04/17 Distributed power generation  Education  Electric power transmission networks  Energy management  Multi agent systems  Power electronics  Power transmission  Reinforcement  Reinforcement learning  Wind power   Cooperative Reinforcement Learning Algorithm to Distributed Power System Based on Multi-Agent (CPCI-S收录) 会议论文 2009 3RD INTERNATIONAL CONFERENCE ON POWER ELECTRONICS SYSTEMS AND APPLICATIONS: ELECTRIC VEHICLE AND GREEN ENERGY 作者:  Gao, La-mei[1];  Zeng, Jun[1];  Wu, Jie[1];  Li, Min[1] 收藏  |  浏览/下载：2/0  |  提交时间：2019/04/17 distributed power  multi-agent  reinforcement learning  Q-learning  joint action learning