中国科学院机构知识库网格系统: 检索

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	Monocular contextual constraint for stereo matching with adaptive weights assignment 期刊论文 OAI收割 IMAGE AND VISION COMPUTING, 2022, 卷号: 121, 页码: 10 作者: Zhang, Chenghao; Meng, Gaofeng; Su, Bing; Xiang, Shiming; Pan, Chunhong \| 收藏 \| 浏览/下载：49/0 \| 提交时间：2022/06/10 Deep learning Stereo matching Monocular contextual constraint Adaptive weights assignment
	Learning to Learn Adaptive Classifier-Predictor for Few-Shot Learning 期刊论文 OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 卷号: 32, 期号: 8, 页码: 3458-3470 作者: Lai, Nan; Kan, Meina; Han, Chunrui; Song, Xingguang; Shan, Shiguang \| 收藏 \| 浏览/下载：43/0 \| 提交时间：2021/12/01 Task analysis Adaptation models Training Predictive models Feature extraction Generators Computational modeling Few-shot learning meta-learning predict classifier weights task-adaptive predictor
	Dense Mapping From an Accurate Tracking SLAM 期刊论文 OAI收割 IEEE/CAA Journal of Automatica Sinica, 2020, 卷号: 7, 期号: 6, 页码: 1565-1574 作者: Weijie Huang; Guoshan Zhang; Xiaowei Han \| 收藏 \| 浏览/下载：15/0 \| 提交时间：2021/03/11 Adaptive weights data association dense mapping hash table simultaneous localization and mapping (SLAM)
	Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition 期刊论文 OAI收割 ELECTRONICS, 2019, 卷号: 8, 期号: 4, 页码: 15 作者: Chen, Ying; Zhang, Zhihao; Zhong, Lei; Chen, Tong; Chen, Juxiang \| 收藏 \| 浏览/下载：72/0 \| 提交时间：2019/06/24 NIR facial expression recognition SE block 3D CNN adaptive feature weights calibration
	Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition 期刊论文 OAI收割 ELECTRONICS, 2019, 卷号: 8, 期号: 4, 页码: 15 作者: Chen, Ying; Zhang, Zhihao; Zhong, Lei; Chen, Tong; Chen, Juxiang \| 收藏 \| 浏览/下载：69/0 \| 提交时间：2019/06/26 NIR facial expression recognition SE block 3D CNN adaptive feature weights calibration
	Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices 期刊论文 OAI收割 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2018, 卷号: 13, 期号: 11, 页码: 2897-2912 作者: Zhang, Qi; Li, Haiqing; Sun, Zhenan; Tan, Tieniu \| 收藏 \| 浏览/下载：62/0 \| 提交时间：2019/12/16 Iris recognition periocular recognition deep feature fusion adaptive weights mobile devices
	Adaptive dynamic programming for robust neural control of unknown continuous-time non-linear systems 期刊论文 OAI收割 IET CONTROL THEORY AND APPLICATIONS, 2017, 卷号: 11, 期号: 14, 页码: 2307-2316 作者: Yang, Xiong; He, Haibo; Liu, Derong; Zhu, Yuanheng \| 收藏 \| 浏览/下载：37/0 \| 提交时间：2017/09/13 Dynamic Programming Robust Control Neurocontrollers Continuous Time Systems Control System Synthesis Nonlinear Control Systems Optimal Control Function Approximation Monte Carlo Methods Closed Loop Systems Asymptotic Stability Adaptive Dynamic Programming Robust Neural Control Design Unknown Continuous-time Nonlinear Systems Ct Nonlinear Systems Adp-based Robust Neural Control Scheme Robust Nonlinear Control Problem Nonlinear Optimal Control Problem Nominal System Adp Algorithm Actor-critic Dual Networks Control Policy Approximation Value Function Approximation Actor Neural Network Weights Critic Nn Weights Monte Carlo Integration Method Closed-loop System Asymptotically Stability
	Robust Visual Cooperative Tracking Using Constrained Adaptive Sparse Representations and Sparse Classifier Grids 期刊论文 OAI收割 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2014, 卷号: 24, 期号: 9, 页码: 1509-1521 作者: Kuang, Jinjun; Zhou, Xi; Gamst, Anthony \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2018/03/15 Adaptive basis construction NormalHedge (NH) sparse representation spatio-temporal weights visual tracking
	An improved adaptive neural network method for control system (EI CONFERENCE) 会议论文 OAI收割 2006 International Conference on Machine Learning and Cybernetics, August 13, 2006 - August 16, 2006, Dalian, China Wang L.-M.; Xie M.-J.; Wu D.-Y. 收藏 \| 浏览/下载：18/0 \| 提交时间：2013/03/25 Classical methods for designing a controller depend on the accuracy of system model. However plant's models and other parts in a physical system can not accurately represent all possible dynamics. Thus the controller designed is usually not the optimal one. In this article a new simple adaptive control method which combines the classical frequency domain method with the neural network theory is proposed. Firstly we can obtain a controller using classical method. Secondly we use the coefficients in digitized controller equation as the initial values of an Adaline network. Finally LMS learning rules is used to adjust the weights adaptively. Experimental results show that this method is very effective in improving the performance of conventional controller. 2006 IEEE.