What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
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 most cited work include:
- Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming (244 citations)
- Adaptive sliding mode fuzzy control for a two-dimensional overhead crane (201 citations)
- Design of a stable sliding-mode controller for a class of second-order underactuated systems (198 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (35.74%)
- Control theory (34.43%)
- Artificial neural network (26.89%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (35.74%)
- Reinforcement learning (23.93%)
- Artificial neural network (26.89%)
In recent papers he was focusing on the following fields of study:
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
- Nash equilibrium which is related to area like Minimax, Nonlinear system, Optimal control, Iterative learning control and Rate of convergence,
- Markov chain most often made with reference to Mathematical optimization.
Dongbin Zhao interconnects Dynamic programming and Differential dynamic programming in the investigation of issues within Nonlinear system.
His work in Dynamic programming addresses issues such as Control theory, which are connected to fields such as Normalization.
Between 2018 and 2021, his most popular works were:
- StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning (54 citations)
- Reinforcement Learning and Deep Learning Based Lateral Control for Autonomous Driving [Application Notes] (33 citations)
- Adaptive Optimal Control of Heterogeneous CACC System With Uncertain Dynamics (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
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.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
