What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Quantum mechanics
Tingwen Huang spends much of his time researching Control theory, Artificial neural network, Exponential stability, Mathematical optimization and Nonlinear system.
He has included themes like Memristor, Complex network and Synchronization in his Control theory study.
Tingwen Huang studies Artificial neural network, focusing on Recurrent neural network in particular.
His Exponential stability research includes themes of Equilibrium point, State and Activation function.
His Mathematical optimization research incorporates elements of Convergence, Rate of convergence, Reinforcement learning and Smart grid.
His Nonlinear system research integrates issues from Control system and Fuzzy logic.
His most cited work include:
- A multiple exp-function method for nonlinear differential equations and its application (381 citations)
- Event-Triggering Sampling Based Leader-Following Consensus in Second-Order Multi-Agent Systems (337 citations)
- Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects (262 citations)
What are the main themes of his work throughout his whole career to date?
Tingwen Huang mostly deals with Control theory, Artificial neural network, Nonlinear system, Exponential stability and Mathematical optimization.
His research in Control theory intersects with topics in Multi-agent system, Stability and Synchronization.
While the research belongs to areas of Multi-agent system, Tingwen Huang spends his time largely on the problem of Network topology, intersecting his research to questions surrounding Topology.
Tingwen Huang has researched Artificial neural network in several fields, including Stability, Memristor and Applied mathematics.
His Nonlinear system study combines topics from a wide range of disciplines, such as Mathematical analysis and Fuzzy logic.
Tingwen Huang interconnects Impulse, Equilibrium point and Exponential function in the investigation of issues within Exponential stability.
He most often published in these fields:
- Control theory (48.63%)
- Artificial neural network (38.08%)
- Nonlinear system (16.99%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial neural network (38.08%)
- Control theory (48.63%)
- Multi-agent system (8.90%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial neural network, Control theory, Multi-agent system, Control theory and Nonlinear system.
His Artificial neural network research is multidisciplinary, relying on both Memristor, Inertial frame of reference, Exponential stability, Synchronization and Applied mathematics.
His Control theory study combines topics in areas such as Observer, Bounded function and Fuzzy logic.
His Multi-agent system research focuses on Network topology and how it connects with Topology, Topology, Upper and lower bounds, Information exchange and Markov process.
His Control theory study integrates concerns from other disciplines, such as Linear matrix inequality and Exponential function.
His research integrates issues of Control system and Fuzzy control system in his study of Nonlinear system.
Between 2019 and 2021, his most popular works were:
- Prescribed Performance Cooperative Control for Multiagent Systems With Input Quantization (81 citations)
- Containment Control of Semi-Markovian Multiagent Systems With Switching Topologies (74 citations)
- Event-Triggered Fuzzy Bipartite Tracking Control for Network Systems Based on Distributed Reduced-Order Observers(Revised manuscript of TFS-2019-1049) (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Quantum mechanics
- Mathematical analysis
Tingwen Huang mainly focuses on Artificial neural network, Control theory, Control theory, Nonlinear system and Synchronization.
Tingwen Huang combines subjects such as Reaction–diffusion system, Computer engineering, Topology, Algorithm and Topology with his study of Artificial neural network.
Particularly relevant to Lyapunov function is his body of work in Control theory.
His work focuses on many connections between Lyapunov function and other disciplines, such as Network topology, that overlap with his field of interest in Estimator.
The Nonlinear system study combines topics in areas such as Control system and Linear system.
His research investigates the link between Synchronization and topics such as Correctness that cross with problems in Network packet, Iterative learning control, Packet loss, Classification of discontinuities and Tracking.
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