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自动化研究所 [19]

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	Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems 期刊论文 IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2023, 页码: 12 作者: Zhang, Yong; Mu, Chaoxu; Zhao, Dongbin 收藏 \| 浏览/下载：6/0 \| 提交时间：2023/11/16 Data models Real-time systems Heuristic algorithms Mathematical models Adaptation models Approximation algorithms Cost function Data generation model feedback relearning control delayed neural network reinforcement learning unmodeled nonlinear system
	Hierarchical optimal control for input-affine nonlinear systems through the formulation of Stackelberg game 期刊论文 INFORMATION SCIENCES, 2020, 卷号: 517, 页码: 1-17 作者: Mu, Chaoxu; Wang, Ke; Zhang, Qichao; Zhao, Dongbin 收藏 \| 浏览/下载：17/0 \| 提交时间：2020/04/07 Nonzero-sum differential game Hierarchical optimization Nonlinear dynamics Stackelberg equilibrium Neural network
	Q-learning solution for optimal consensus control of discrete-time multiagent systems using reinforcement learning. 期刊论文 Journal of the Franklin Institute, 2019, 卷号: Vol.356 No.13, 页码: 6946-6967 作者: Mu, Chaoxu; Zhao, Qian; Gao, Zhongke; Sun, Changyin 收藏 \| 浏览/下载：4/0 \| 提交时间：2019/11/21
	EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation. 期刊论文 IEEE Transactions on Neural Networks & Learning Systems, 2019, 卷号: Vol.30 No.9, 页码: 2755-2763 作者: Gao, Zhongke; Wang, Xinmin; Yang, Yuxuan; Mu, Chaoxu; Cai, Qing 收藏 \| 浏览/下载：28/0 \| 提交时间：2019/11/21 Brain modeling Brain–computer interface (BCI) Convolution convolutional neural network (CNN) deep learning (DL) electroencephalogram (EEG) Electroencephalography Fatigue fatigue driving Feature extraction Physiology spatio–temporal data Task analysis
	Data-Driven Finite-Horizon Approximate Optimal Control for Discrete-Time Nonlinear Systems Using Iterative HDP Approach 期刊论文 IEEE TRANSACTIONS ON CYBERNETICS, 2018, 卷号: 48, 期号: 10, 页码: 2948-2961 作者: Mu, Chaoxu; Wang, Ding; He, Haibo 收藏 \| 浏览/下载：33/0 \| 提交时间：2019/12/16 Adaptive Dynamic Programming (Adp) Data-driven Control Finite-horizon Optimal Control Heuristic Dynamic Programming (Hdp) Learning Control Neural Networks
	Decentralized adaptive optimal stabilization of nonlinear systems with matched interconnections 期刊论文 SOFT COMPUTING, 2018, 卷号: 22, 期号: 8, 页码: 2705-2715 作者: Mu, Chaoxu; Sun, Changyin; Wang, Ding; Song, Aiguo; Qian, Chengshan 收藏 \| 浏览/下载：21/0 \| 提交时间：2018/10/10 Adaptive Dynamic Programming (Adp) Interconnected Nonlinear Systems Neural Networks Decentralized Control Matched Interconnections
	On Mixed Data and Event Driven Design for Adaptive-Critic-Based Nonlinear H-infinity Control 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 4, 页码: 993-1005 作者: Wang, Ding; Mu, Chaoxu; Liu, Derong; Ma, Hongwen 收藏 \| 浏览/下载：48/0 \| 提交时间：2018/10/10 Adaptive Critic Designs Adaptive Dynamic Programming (Adp) Data Driven Control Event Driven Control Hamilton-jacobi-isaacs (Hji) Equation Neural Network Identification Nonlinear H-infinity Control Zero-sum Game
	Neural Network Learning and Robust Stabilization of Nonlinear Systems With Dynamic Uncertainties 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 4, 页码: 1342-1351 作者: Wang, Ding; Liu, Derong; Mu, Chaoxu; Zhang, Yun 收藏 \| 浏览/下载：17/0 \| 提交时间：2018/10/10 Adaptive Critic Dynamical Uncertainty Learning Systems Neural Networks Optimal Control Robust Stabilization
	A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System 期刊论文 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2018, 卷号: Vol.14 No.9, 页码: 3982-3988 作者: Gao, Zhongke; Dang, Weidong; Mu, Chaoxu; Yang, Yuxuan; Li, Shan 收藏 \| 浏览/下载：7/0 \| 提交时间：2019/12/26 Industrial multiphase flow information fusion multiplex network signal analysis
	Adaptive-Critic-Based Robust Trajectory Tracking of Uncertain Dynamics and Its Application to a Spring-Mass-Damper System 期刊论文 IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2018, 卷号: 65, 期号: 1, 页码: 654-663 作者: Wang, Ding; Mu, Chaoxu 收藏 \| 浏览/下载：32/0 \| 提交时间：2019/12/16 Adaptive critic design neural networks optimal control robust trajectory tracking self-learning control system uncertainty

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