中国科学院重庆绿色智能技术研究院机构知识库

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限定条件	资助项目：Pioneer Hundred Talents Program of Chinese Academy of Sciences    作者：罗辛    第一作者

已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择期刊影响因子升序期刊影响因子降序作者升序作者降序题名升序题名降序WOS被引频次升序WOS被引频次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
	Distributed and Time-Delayed k-Winner-Take-All Network for Competitive Coordination of Multiple Robots 期刊论文 IEEE TRANSACTIONS ON CYBERNETICS, 2022, 页码: 12 作者:  Jin, Long;  Liang, Siqi;  Luo, Xin;  Zhou, Mengchu 收藏  |  浏览/下载：63/0  |  提交时间：2022/08/22 Mathematical models  Delay effects  Task analysis  Robot kinematics  Multi-robot systems  Robustness  Heuristic algorithms  Competitive coordination  distributed control  intelligent network  k-winner-take-all (kWTA)  multirobot system  optimization  time delay
	Distributed Competition of Multi-Robot Coordination Under Variable and Switching Topologies 期刊论文 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2021, 页码: 12 作者:  Jin, Long;  Qi, Yimeng;  Luo, Xin;  Li, Shuai;  Shang, Mingsheng 收藏  |  浏览/下载：60/0  |  提交时间：2022/08/22 Robot kinematics  Robots  Topology  Multi-robot systems  Heuristic algorithms  Task analysis  Collaboration  Multi-robot coordination  winner-take-all  gradient neural network  distributed control
	Robust k-WTA Network Generation, Analysis, and Applications to Multiagent Coordination 期刊论文 IEEE TRANSACTIONS ON CYBERNETICS, 2021, 页码: 13 作者:  Qi, Yimeng;  Jin, Long;  Luo, Xin;  Shi, Yang;  Liu, Mei 收藏  |  浏览/下载：45/0  |  提交时间：2022/08/22 Task analysis  Robustness  Biological neural networks  Resource management  Dynamic scheduling  Convergence  Recurrent neural networks  Consensus filter  k-winner-take-all (k-WTA)  multiagent coordination  theoretical analysis
	An L-1-and-L-2-Norm-Oriented Latent Factor Model for Recommender Systems 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 页码: 14 作者:  Wu, Di;  Shang, Mingsheng;  Luo, Xin;  Wang, Zidong 收藏  |  浏览/下载：42/0  |  提交时间：2022/08/22 High-dimensional and sparse (HiDS) matrix  latent factor (LF) analysis  L-1 norm  L-2 norm  recommender system (RS)
	Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data 期刊论文 IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2021, 卷号: 8, 期号: 4, 页码: 796-805 作者:  Wu, Di;  Luo, Xin 收藏  |  浏览/下载：82/0  |  提交时间：2021/05/17 High-dimensional and sparse matrix  L-1-norm  L-2-norm  latent factor model  recommender system  smooth L-1-norm