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A Survey of Cognitive Architectures in the Past 20 Years 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2018, 卷号: 48, 期号: 12, 页码: 3280-3290
作者:  Ye, Peijun;  Wang, Tao;  Wang, Fei-Yue
浏览  |  Adobe PDF(734Kb)  |  收藏  |  浏览/下载：700/255  |  提交时间：2019/01/08
Agent  artificial intelligence (Al)  cognitive architecture (CA)  survey  
Animated Construction of Chinese Brush Paintings 期刊论文
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2018, 卷号: 24, 期号: 12, 页码: 3019-3031
作者:  Tang, Fan;  Dong, Weiming;  Meng, Yiping;  Mei, Xing;  Huang, Feiyue;  Zhang, Xiaopeng;  Deussen, Oliver
浏览  |  Adobe PDF(1510Kb)  |  收藏  |  浏览/下载：540/139  |  提交时间：2019/01/08
Chinese brush painting  animation  drawing analysis  art teaching  
Optimized Multi-Agent Formation Control Based on an Identifier-Actor--Critic Reinforcement Learning Algorithm 期刊论文
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2018, 卷号: 26, 期号: 5, 页码: 2719-2731
作者:  Wen, Guoxing;  Chen, C. L. Philip;  Feng, Jun;  Zhou, Ning
收藏  |  浏览/下载：242/0  |  提交时间：2019/12/16
Fuzzy logic systems (FLSs)  identifier-actor-critic architecture  multi-agent formation  optimized formation control  reinforcement learning (RL)  
A Brain-Inspired Decision-Making Spiking Neural Network and Its Application in Unmanned Aerial Vehicle 期刊论文
Frontiers in Neurorobotics, 2018, 期号: 12, 页码: 56
作者:  Zhao Feifei;  Zeng Yi;  Xu Bo
浏览  |  Adobe PDF(2166Kb)  |  收藏  |  浏览/下载：301/81  |  提交时间：2019/04/22
Spiking Neural Network  Brain-inspired Decision-making  Dopamine Regulation  Multiple Brain Areas Coordination  Reinforcement Learning  UAV Autonomous Learning  
Brain-Inspired Motion Learning in Recurrent Neural Network With Emotion Modulation 期刊论文
IEEE Transactions on Cognitive and Developmental Systems, 2018, 卷号: 10, 期号: 4, 页码: 1153 - 1164
作者:  Huang, Xiao;  Wu, Wei;  Qiao, Hong;  Ji, Yidao
浏览  |  Adobe PDF(2073Kb)  |  收藏  |  浏览/下载：205/67  |  提交时间：2020/06/09
Brain-inspired model  emotion  motion learning  recurrent neural network  
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：372/114  |  提交时间：2018/10/10
Adaptive Control  Adaptive Dynamic Programming  Constraints  Critic-only  Data-based  Optimal Control  Q-learning  
Connectivity preserved nonlinear time-delayed multiagent systems using neural networks and event-based mechanism 期刊论文
NEURAL COMPUTING & APPLICATIONS, 2018, 卷号: 29, 期号: 9, 页码: 361-369
作者:  Ma, Hongwen;  Wang, Ding;  Ding Wang
浏览  |  Adobe PDF(1038Kb)  |  收藏  |  浏览/下载：405/107  |  提交时间：2017/05/03
Connectivity Preservation  Event-based Control  Multiagent Systems  Neural Networks  
Distributed event-triggered synchronization for robotic teleoperation systems with randomly occurring gain fluctuations 期刊论文
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2018, 卷号: 15, 期号: 2, 页码: 1729881418770881
作者:  Ma, Chao;  Kang, Erlong
Adobe PDF(636Kb)  |  收藏  |  浏览/下载：261/48  |  提交时间：2019/12/16
Asynchronous event-triggered scheme  event-triggered synchronization  robotic teleoperation systems  randomly occurring gain fluctuations  Lyapunov-Krasovskii method  
Computational botany: advancing plant science through functional-structural plant modelling PREFACE 期刊论文
ANNALS OF BOTANY, 2018, 卷号: 121, 期号: 5, 页码: 767-772
作者:  Evers, Jochem B.;  Letort, Veronique;  Renton, Michael;  Kang, Mengzhen
浏览  |  Adobe PDF(827Kb)  |  收藏  |  浏览/下载：238/59  |  提交时间：2018/10/10
Functional–structural Plant Modelling  
Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 4, 页码: 1226-1238
作者:  Wei, Qinglai;  Li, Benkai;  Song, Ruizhuo
浏览  |  Adobe PDF(2475Kb)  |  收藏  |  浏览/下载：379/123  |  提交时间：2017/02/23
Adaptive Critic Designs  Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Generalized Policy Iteration (Gpi)  Neural Networks  Neurodynamic Programming  Nonlinear Systems  Optimal Control  Reinforcement Learning