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浏览/检索结果: 共9条，第1-9条 帮助 限定条件 发表日期：2019    专题：自动化研究所    第一署名单位    第一作者单位    通讯作者单位     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 Dual L-1-Normalized Context Aware Tensor Power Iteration and Its Applications to Multi-object Tracking and Multi-graph Matching 期刊论文 INTERNATIONAL JOURNAL OF COMPUTER VISION, 2019, 页码: 33 作者:  Hu, Weiming;  Shi, Xinchu;  Zhou, Zongwei;  Xing, Junliang;  Ling, Haibin 收藏  |  浏览/下载：65/0  |  提交时间：2019/12/16 Multi-dimensional assignment  Context  hyper-context aware tensor power iteration  Multi-object tracking  Multi-graph matching   Optimized Adaptive Nonlinear Tracking Control Using Actor-Critic Reinforcement Learning Strategy 期刊论文 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2019, 卷号: 15, 期号: 9, 页码: 4969-4977 作者:  Wen, Guoxing;  Chen, C. L. Philip;  Ge, Shuzhi Sam;  Yang, Hongli;  Liu, Xiaoguang 收藏  |  浏览/下载：61/0  |  提交时间：2019/12/16 Lyapunov function  neural networks (NNs)  nonlinear systems  optimized tracking control  reinforcement learning (RL) of actor-critic architecture   Rank-1 Tensor Approximation for High-Order Association in Multi-target Tracking 期刊论文 INTERNATIONAL JOURNAL OF COMPUTER VISION, 2019, 卷号: 127, 期号: 8, 页码: 1063-1083 作者:  Shi, Xinchu;  Ling, Haibin;  Pang, Yu;  Hu, Weiming;  Chu, Peng 收藏  |  浏览/下载：73/0  |  提交时间：2019/12/16 Multi-target tracking  Multi-dimensional assignment  Rank-1 tensor approximation  Data association   Adaptive Neural State-Feedback Tracking Control of Stochastic Nonlinear Switched Systems: An Average Dwell-Time Method 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 4, 页码: 1076-1087 作者:  Niu, Ben;  Wang, Ding;  Alotaibi, Naif D.;  Alsaadi, Fuad E. 收藏  |  浏览/下载：53/0  |  提交时间：2019/12/16 Adaptive tracking control  average dwell time (ADT)  neural networks (NNs)  nonstrict-feedback structure  stochastic nonlinear systems  switched nonlinear systems   Universal Approximation Capability of Broad Learning System and Its Structural Variations 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 4, 页码: 1191-1204 作者:  Chen, C. L. Philip;  Liu, Zhulin;  Feng, Shuang 收藏  |  浏览/下载：44/0  |  提交时间：2019/12/16 Broad learning system (BLS)  deep learning  face recognition  functional link neural networks (FLNNs)  non-linear function approximation  time-variant big data modeling  universal approximation   A Regularized Variable Projection Algorithm for Separable Nonlinear Least-Squares Problems 期刊论文 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2019, 卷号: 64, 期号: 2, 页码: 526-537 作者:  Chen, Guang-Yong;  Gan, Min;  Chen, C. L. Philip;  Li, Han-Xiong 收藏  |  浏览/下载：48/0  |  提交时间：2019/07/12 Data fitting  regularization  separable nonlinear least squares (SNLLS)  variable projection (VP)  weighted generalized cross validation (WGCV)   Variational Bayesian Learning for Dirichlet Process Mixture of Inverted Dirichlet Distributions in Non-Gaussian Image Feature Modeling 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 2, 页码: 449-463 作者:  Ma, Zhanyu;  Lai, Yuping;  Kleijn, W. Bastiaan;  Song, Yi-Zhe;  Wang, Liang 收藏  |  浏览/下载：51/0  |  提交时间：2019/07/12 Bayesian estimation  computer vision  Dirichlet process (DP) mixture  inverted Dirichlet distribution  variational learning   Rough extreme learning machine: A new classification method based on uncertainty measure 期刊论文 NEUROCOMPUTING, 2019, 卷号: 325, 页码: 269-282 作者:  Feng, Lin;  Xu, Shuliang;  Wang, Feilong;  Liu, Shenglan;  Qiao, Hong 收藏  |  浏览/下载：53/0  |  提交时间：2019/01/08 Extreme learning machine  Rough set  Attribute reduction  Classification  Neural network   Adaptive Reinforcement Learning Control Based on Neural Approximation for Nonlinear Discrete-Time Systems With Unknown Nonaffine Dead-Zone Input 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 卷号: 30, 期号: 1, 页码: 295-305 作者:  Liu, Yan-Jun;  Li, Shu;  Tong, Shaocheng;  Chen, C. L. Philip 收藏  |  浏览/下载：45/0  |  提交时间：2019/07/12 Discrete-time systems  neural networks (NNs)  nonlinear systems  optimal control  reinforcement learning