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Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 10, 页码: 4791-4801
作者:  Li, Shuai;  Zhou, MengChu;  Luo, Xin
Adobe PDF(2696Kb)  |  收藏  |  浏览/下载:291/0  |  提交时间:2018/11/01
Dual neural network  kinematic control  redundancy resolution  robotic manipulator  
Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 10, 页码: 4791-4801
作者:  Li, Shuai;  Zhou, MengChu;  Luo, Xin
Adobe PDF(2696Kb)  |  收藏  |  浏览/下载:286/0  |  提交时间:2019/06/26
Dual neural network  kinematic control  redundancy resolution  robotic manipulator  
An Inherently Nonnegative Latent Factor Model for High-Dimensional and Sparse Matrices from Industrial Applications 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 卷号: 14, 期号: 5, 页码: 2011-2022
作者:  Luo, Xin;  Zhou, MengChu;  Li, Shuai;  Shang, MingSheng
收藏  |  浏览/下载:507/0  |  提交时间:2018/07/02
Big data  high-dimensional and sparse matrix  learning algorithms  missing-data estimation  nonnegative latent factor analysis  optimization methods recommender system  
A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 卷号: 14, 期号: 3, 页码: 909-920
作者:  Wu, Di;  Luo, Xin;  Wang, Guoyin;  Shang, Mingsheng;  Yuan, Ye;  Yan, Huyong
收藏  |  浏览/下载:212/0  |  提交时间:2018/06/04
Differential evolution (DE)  general framework  industrial application  positioning optimization  self-labeled  semi-supervised classification (SSC)  
Incremental Slope-one recommenders 期刊论文
NEUROCOMPUTING, 2018, 卷号: 272, 页码: 606-618
作者:  Wang, Qing-Xian;  Luo, Xin;  Li, Yan;  Shi, Xiao-Yu;  Gu, Liang;  Shang, Ming-Sheng
收藏  |  浏览/下载:157/0  |  提交时间:2018/03/05
Collaborative Filtering  Slope-one  Recommender System  Dynamic Datasets  Incremental Recommenders