What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mathematical analysis
Yiguang Hong mainly focuses on Control theory, Multi-agent system, Lyapunov function, Mathematical optimization and Nonlinear system.
His work on Control theory as part of general Control theory research is frequently linked to Context, bridging the gap between disciplines.
The concepts of his Multi-agent system study are interwoven with issues in Topology, Internal model, Network topology, Autonomous agent and Convex analysis.
His Lyapunov function research includes themes of Parametric statistics, Stability, Graph theory, Numerical analysis and Adaptive control.
His Mathematical optimization research incorporates elements of Distributed algorithm, Convergence, Electric power system and Convex optimization.
Many of his research projects under Nonlinear system are closely connected to Control with Control, tying the diverse disciplines of science together.
His most cited work include:
- Tracking control for multi-agent consensus with an active leader and variable topology (1518 citations)
- Technical communique: Distributed observers design for leader-following control of multi-agent networks (893 citations)
- Leader-following coordination of multi-agent systems with coupling time delays (733 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Control theory, Mathematical optimization, Multi-agent system, Nonlinear system and Distributed algorithm.
Control theory is closely attributed to Control engineering in his work.
His Mathematical optimization study also includes fields such as
- Autonomous agent most often made with reference to Convergence,
- Convex optimization, which have a strong connection to Algorithm,
- Convex function that intertwine with fields like Subgradient method and Rate of convergence.
His work in Multi-agent system covers topics such as Network topology which are related to areas like Topology.
The various areas that Yiguang Hong examines in his Nonlinear system study include Control system, Class, Stability and Parametric statistics.
His studies deal with areas such as Algorithm design, Strongly connected component, Nash equilibrium, Applied mathematics and Computation as well as Distributed algorithm.
He most often published in these fields:
- Control theory (40.50%)
- Mathematical optimization (27.55%)
- Multi-agent system (27.00%)
What were the highlights of his more recent work (between 2018-2021)?
- Distributed algorithm (16.25%)
- Mathematical optimization (27.55%)
- Convergence (15.70%)
In recent papers he was focusing on the following fields of study:
Yiguang Hong mainly investigates Distributed algorithm, Mathematical optimization, Convergence, Applied mathematics and Computation.
His Distributed algorithm study integrates concerns from other disciplines, such as Matrix, Convex function, Convex optimization, Rate of convergence and Optimization problem.
His Mathematical optimization research is multidisciplinary, incorporating elements of Function, Differentiable function and Resource allocation.
His Convergence research includes themes of Control theory, Strongly connected component, Bounded function, Linear programming and Upper and lower bounds.
His Control theory research is multidisciplinary, relying on both Stability and Ideal.
The various areas that Yiguang Hong examines in his Topology study include Stability and Multi-agent system.
Between 2018 and 2021, his most popular works were:
- A survey of distributed optimization (94 citations)
- Optimal Output Consensus of High-Order Multiagent Systems With Embedded Technique (35 citations)
- Distributed Computation of Linear Matrix Equations: An Optimization Perspective (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mathematical analysis
- Statistics
Yiguang Hong mostly deals with Mathematical optimization, Convergence, Convex function, Distributed algorithm and Optimization problem.
His Differential inclusion and Stochastic optimization algorithm study in the realm of Mathematical optimization interacts with subjects such as Global function.
He has included themes like Matrix, Strongly connected component, Gradient method and Applied mathematics in his Distributed algorithm study.
His Acceleration study combines topics from a wide range of disciplines, such as Consensus and Control theory.
His Consensus study improves the overall literature in Multi-agent system.
His Control theory research includes elements of State and Covariance.
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