What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Machine learning
His primary areas of investigation include Mathematical optimization, Optimal control, Nonlinear system, Artificial neural network and Control theory.
His study in Dynamic programming and Bellman equation is carried out as part of his Mathematical optimization studies.
Ding Wang combines subjects such as Lyapunov function, Robust control, Iterative method, Intelligent control and Adaptive control with his study of Optimal control.
His study in Nonlinear system is interdisciplinary in nature, drawing from both Stability, Algorithm and Dynamical system.
His Artificial neural network research is multidisciplinary, relying on both Control theory and Linear-quadratic-Gaussian control.
His research integrates issues of Nash equilibrium and Reinforcement learning in his study of Control theory.
His most cited work include:
- Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming (244 citations)
- Decentralized Stabilization for a Class of Continuous-Time Nonlinear Interconnected Systems Using Online Learning Optimal Control Approach (193 citations)
- Neural-Network-Based Online HJB Solution for Optimal Robust Guaranteed Cost Control of Continuous-Time Uncertain Nonlinear Systems (166 citations)
What are the main themes of his work throughout his whole career to date?
Ding Wang focuses on Artificial neural network, Nonlinear system, Optimal control, Control theory and Mathematical optimization.
His Artificial neural network research incorporates themes from Discrete time and continuous time, Control engineering, Control, Intelligent control and Reinforcement learning.
His studies deal with areas such as Differential game, Approximation algorithm and System dynamics as well as Nonlinear system.
His Optimal control research includes themes of Stability, Dynamic programming and Adaptive control.
The various areas that Ding Wang examines in his Dynamic programming study include Dual control theory and Bellman equation.
The concepts of his Mathematical optimization study are interwoven with issues in Algorithm and Computational intelligence.
He most often published in these fields:
- Artificial neural network (72.16%)
- Nonlinear system (69.89%)
- Optimal control (68.75%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial neural network (72.16%)
- Optimal control (68.75%)
- Nonlinear system (69.89%)
In recent papers he was focusing on the following fields of study:
Ding Wang mainly focuses on Artificial neural network, Optimal control, Nonlinear system, Control theory and Mathematical optimization.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Control theory, Discrete time and continuous time and Adaptive system.
Ding Wang has included themes like Dynamical systems theory, Dynamic programming, Robustness and Bellman equation in his Optimal control study.
His work on Stability theory and Exponential stability as part of general Nonlinear system study is frequently linked to Uniform boundedness, therefore connecting diverse disciplines of science.
His Control theory study frequently draws connections between related disciplines such as Tracking.
His Iterative method study, which is part of a larger body of work in Mathematical optimization, is frequently linked to Population and Process, bridging the gap between disciplines.
Between 2019 and 2021, his most popular works were:
- Self-Learning Optimal Regulation for Discrete-Time Nonlinear Systems Under Event-Driven Formulation (35 citations)
- Adaptive Neural Output-Feedback Controller Design of Switched Nonlower Triangular Nonlinear Systems With Time Delays (21 citations)
- Robust Policy Learning Control of Nonlinear Plants With Case Studies for a Power System Application (12 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Artificial intelligence
- Machine learning
Ding Wang spends much of his time researching Nonlinear system, Control theory, Artificial neural network, Optimal control and Stability.
The Nonlinear system study combines topics in areas such as Control theory, Dynamic programming and Adaptive system.
The study incorporates disciplines such as Stability criterion, Exponential stability and Trajectory in addition to Control theory.
His Dynamic programming research is multidisciplinary, relying on both Recurrent neural network and Actuator, Robustness, Adaptive learning, Artificial intelligence.
His work deals with themes such as Approximation algorithm, Robust control and Electric power system, which intersect with Optimal control.
His Stability research is multidisciplinary, incorporating perspectives in Discrete time and continuous time, State observer and Bellman equation.
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