What is he best known for?
The fields of study he is best known for:
- Control theory
- Mathematical analysis
- Algorithm
Control theory, Linear matrix inequality, Artificial neural network, Exponential stability and Discrete time and continuous time are his primary areas of study.
His work on Lyapunov function and Equilibrium point as part of his general Control theory study is frequently connected to Synchronization and Stochastic process, thereby bridging the divide between different branches of science.
His work in Lyapunov function addresses subjects such as Nonlinear control, which are connected to disciplines such as Filtering problem and Robust control.
His studies examine the connections between Equilibrium point and genetics, as well as such issues in Monotonic function, with regards to Stability and Bounded function.
His research on Artificial neural network frequently links to adjacent areas such as Mathematical optimization.
His study in Exponential stability is interdisciplinary in nature, drawing from both Recurrent neural network, Stability conditions, Applied mathematics and Stability theory.
His most cited work include:
- Global exponential stability of generalized recurrent neural networks with discrete and distributed delays (601 citations)
- Global Synchronization for Discrete-Time Stochastic Complex Networks With Randomly Occurred Nonlinearities and Mixed Time Delays (428 citations)
- Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays (425 citations)
What are the main themes of his work throughout his whole career to date?
Yurong Liu mostly deals with Control theory, Artificial neural network, Linear matrix inequality, Exponential stability and Discrete time and continuous time.
His work on Nonlinear system and Lyapunov function as part of general Control theory research is frequently linked to Stochastic process and Complex network, bridging the gap between disciplines.
In his study, Uniqueness is strongly linked to Equilibrium point, which falls under the umbrella field of Artificial neural network.
As part of the same scientific family, Yurong Liu usually focuses on Linear matrix inequality, concentrating on Monotonic function and intersecting with Differentiable function.
The Exponential stability study combines topics in areas such as Recurrent neural network, Stochastic neural network, Stability theory, Applied mathematics and Stability conditions.
His Discrete time and continuous time research is multidisciplinary, relying on both State, Lyapunov stability and Numerical stability.
He most often published in these fields:
- Control theory (58.10%)
- Artificial neural network (33.33%)
- Linear matrix inequality (32.38%)
What were the highlights of his more recent work (between 2018-2021)?
- Control theory (58.10%)
- Nonlinear system (20.00%)
- Applied mathematics (20.00%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Control theory, Nonlinear system, Applied mathematics, Artificial neural network and Topology.
His study in the field of Markov jump also crosses realms of Probability distribution.
His Applied mathematics study incorporates themes from Equilibrium point, Exponential stability and Lyapunov function.
His Artificial neural network study frequently draws parallels with other fields, such as Stability.
His Bounded function study combines topics from a wide range of disciplines, such as Discrete time and continuous time and Lyapunov stability.
His Uniqueness research focuses on Quaternion and how it relates to Linear matrix inequality.
Between 2018 and 2021, his most popular works were:
- Distributed filtering for nonlinear time‐delay systems over sensor networks subject to multiplicative link noises and switching topology (56 citations)
- Sampled-data control for a class of linear time-varying systems (54 citations)
- Event-Triggered Partial-Nodes-Based State Estimation for Delayed Complex Networks With Bounded Distributed Delays (48 citations)
In his most recent research, the most cited papers focused on:
- Algorithm
- Control theory
- Mathematical analysis
The scientist’s investigation covers issues in Applied mathematics, Topology, Nonlinear system, Stability and Artificial neural network.
His work carried out in the field of Applied mathematics brings together such families of science as Time complexity and Exponential stability, Halanay inequality.
The Topology study combines topics in areas such as Stability theory, Linear system and Filter design.
His Artificial neural network research incorporates elements of Equilibrium point, Quaternion and Lyapunov function.
His work in Discrete time and continuous time covers topics such as Exponential function which are related to areas like Bounded function.
His State research is multidisciplinary, incorporating perspectives in Class and Control theory.
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