2020 - IEEE Fellow For contributions to adaptive dynamic programming and reinforcement learning
His primary areas of study are Artificial neural network, Control theory, Control theory, Mathematical optimization and Optimal control. The subject of his Artificial neural network research is within the realm of Artificial intelligence. Dongbin Zhao works mostly in the field of Control theory, limiting it down to topics relating to Control engineering and, in certain cases, Metering mode and Queueing theory.
Dongbin Zhao has researched Control theory in several fields, including Gas tungsten arc welding, Cruise control, Motion control, Bounded function and Fuzzy logic. His Mathematical optimization research is multidisciplinary, incorporating perspectives in Intelligent control and Algorithm design. The concepts of his Optimal control study are interwoven with issues in Bellman equation and Nonlinear system.
His main research concerns Artificial intelligence, Control theory, Artificial neural network, Reinforcement learning and Control theory. His Artificial intelligence research includes elements of Machine learning, Computer vision and Pattern recognition. While the research belongs to areas of Control theory, he spends his time largely on the problem of Control engineering, intersecting his research to questions surrounding Mobile manipulator.
The Artificial neural network study combines topics in areas such as Dynamic programming, Mathematical optimization, Optimal control and State. His research integrates issues of Task analysis, Function and Convolutional neural network in his study of Reinforcement learning. His work deals with themes such as Stability, Fuzzy logic and Mobile robot, which intersect with Control theory.
Dongbin Zhao mainly focuses on Artificial intelligence, Reinforcement learning, Artificial neural network, Deep learning and Segmentation. Dongbin Zhao combines subjects such as Machine learning, Computer vision and Pattern recognition with his study of Artificial intelligence. His Reinforcement learning study integrates concerns from other disciplines, such as Supervised learning, Task, Function and Algorithm.
His Artificial neural network study also includes fields such as
Dongbin Zhao spends much of his time researching Artificial intelligence, Reinforcement learning, Convolutional neural network, Artificial neural network and Deep learning. His research combines Task analysis and Artificial intelligence. His study explores the link between Reinforcement learning and topics such as Function that cross with problems in Trajectory, Rule-based system and Task.
His Convolutional neural network study combines topics in areas such as Contextual image classification and Informatics. His work deals with themes such as State space, Nonlinear system, Transfer of learning, Rate of convergence and Big data, which intersect with Artificial neural network. His Deep learning research is multidisciplinary, incorporating perspectives in Backpropagation and Viewpoints.
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Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming
Ding Wang;Derong Liu;Qinglai Wei;Dongbin Zhao.
Design of a stable sliding-mode controller for a class of second-order underactuated systems
W. Wang;J. Yi;D. Zhao;D. Liu.
IEE Proceedings - Control Theory and Applications (2004)
Adaptive sliding mode fuzzy control for a two-dimensional overhead crane
Diantong Liu;Jianqiang Yi;Dongbin Zhao;Wei Wang.
Computational Intelligence in Urban Traffic Signal Control: A Survey
Dongbin Zhao;Yujie Dai;Zhen Zhang.
systems man and cybernetics (2012)
Neural-Network-Based Optimal Control for a Class of Unknown Discrete-Time Nonlinear Systems Using Globalized Dual Heuristic Programming
Derong Liu;Ding Wang;Dongbin Zhao;Qinglai Wei.
IEEE Transactions on Automation Science and Engineering (2012)
A computed torque controller for uncertain robotic manipulator systems: Fuzzy approach
Zuoshi Song;Jianqiang Yi;Dongbin Zhao;Xinchun Li.
Fuzzy Sets and Systems (2005)
BP neural network prediction-based variable-period sampling approach for networked control systems
Jianqiang Yi;Qian Wang;Dongbin Zhao;John T. Wen.
international conference on intelligent computing (2007)
Building Energy Consumption Prediction: An Extreme Deep Learning Approach
Chengdong Li;Zixiang Ding;Dongbin Zhao;Jianqiang Yi.
Data-Based Adaptive Critic Designs for Nonlinear Robust Optimal Control With Uncertain Dynamics
Ding Wang;Derong Liu;Qichao Zhang;Dongbin Zhao.
systems man and cybernetics (2016)
Trajectory Tracking Control of Omnidirectional Wheeled Mobile Manipulators: Robust Neural Network-Based Sliding Mode Approach
Dong Xu;Dongbin Zhao;Jianqiang Yi;Xiangmin Tan.
systems man and cybernetics (2009)
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