中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共26条，第1-10条 帮助

限定条件        
已选(0)清除 条数/页：   排序方式：
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 1, 页码: 10-24
作者:  Chen, C. L. Philip;  Liu, Zhulin
收藏  |  浏览/下载：200/0  |  提交时间：2019/12/16
Big data  big data modeling  broad learning system (BLS)  deep learning  incremental learning  random vector functional-link neural networks (RVFLNN)  single layer feedforward neural networks (SLFN)  singular value decomposition (SVD)  
Collaborative Deconvolutional Neural Networks for Joint Depth Estimation and Semantic Segmentation 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 11, 页码: 5655-5666
作者:  Liu, Jing;  Wang, Yuhang;  Li, Yong;  Fu, Jun;  Li, Jiangyun;  Lu, Hanqing
收藏  |  浏览/下载：275/0  |  提交时间：2019/12/16
Deconvolutional neural network (DCNN)  depth estimation  fully connected conditional random field (CRF)  pointwise bilinear layer  semantic segmentation  soft mapping strategy  
Quantized CNN: A Unified Approach to Accelerate and Compress Convolutional Networks 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 10, 页码: 4730-4743
作者:  Cheng, Jian;  Wu, Jiaxiang;  Leng, Cong;  Wang, Yuhang;  Hu, Qinghao
收藏  |  浏览/下载：290/0  |  提交时间：2019/12/16
Acceleration And Compression  Convolutional Neural Network (Cnn)  Mobile Devices  Product Quantization  
Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 8, 页码: 3658-3668
作者:  Wang, Huanqing;  Liu, Peter Xiaoping;  Li, Shuai;  Wang, Ding
收藏  |  浏览/下载：184/0  |  提交时间：2019/12/16
Adaptive neural control  backstepping  nonlower triangular nonlinear systems  output-feedback control  
Multiview Clustering via Unified and View-Specific Embeddings Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 11, 页码: 5541-5553
作者:  Yin, Qiyue;  Wu, Shu;  Wang, Liang
收藏  |  浏览/下载：194/0  |  提交时间：2019/10/10
Incomplete multiview data  knowledge graph embedding  multiview learning  subspace learning  
Learning and Guaranteed Cost Control With Event-Based Adaptive Critic Implementation 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 12, 页码: 6004-6014
作者:  Wang, Ding;  Liu, Derong
收藏  |  浏览/下载：258/0  |  提交时间：2019/07/12
Adaptive dynamic programming  event-based design  guaranteed cost control  optimal control  self-learning technique  
Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 6, 页码: 2099-2111
作者:  Luo, Biao;  Liu, Derong;  Wu, Huai-Ning
浏览  |  Adobe PDF(1045Kb)  |  收藏  |  浏览/下载：379/116  |  提交时间：2018/10/10
Adaptive Control  Adaptive Dynamic Programming  Constraints  Critic-only  Data-based  Optimal Control  Q-learning  
Special Issue on Deep Reinforcement Learning and Adaptive Dynamic Programming 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 6, 页码: 2038-2041
作者:  Zhao, Dongbin;  Liu, Derong;  Lewis, F. L.;  Principe, Jose C.;  Squartini, Stefano
收藏  |  浏览/下载：204/0  |  提交时间：2018/10/10
Computational Model Based on Neural Network of Visual Cortex for Human Action Recognition 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 5, 页码: 1427-1440
作者:  Liu, Haihua;  Shu, Na;  Tang, Qiling;  Zhang, Wensheng
收藏  |  浏览/下载：197/0  |  提交时间：2018/10/10
Action Recognition  Classical Receptive Field (Rf)  Spiking Neural Networks (Snns)  Surround Suppression  Visual Cortex  
Manifold Regularized Reinforcement Learning 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 4, 页码: 932-943
作者:  Li, Hongliang;  Liu, Derong;  Wang, Ding
收藏  |  浏览/下载：179/0  |  提交时间：2018/10/10
Adaptive Dynamic Programming  Approximate Dynamic Programming  Approximate Policy Iteration (Api)  Manifold Regularization  Reinforcement Learning (Rl)