What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Statistics
His main research concerns Control theory, Stochastic process, Linear matrix inequality, Nonlinear system and Exponential stability.
The concepts of his Control theory study are interwoven with issues in Artificial neural network, Filter and Random variable.
His Stochastic process research includes elements of Probability distribution, Estimator, Probabilistic logic and Complex network.
His Linear matrix inequality research incorporates elements of MATLAB, Monotonic function, Kronecker product and Applied mathematics.
His Nonlinear system research integrates issues from Control system, Markov process, Stability, Bernoulli's principle and Algebraic number.
As a part of the same scientific study, Zidong Wang usually deals with the Exponential stability, concentrating on Lyapunov function and frequently concerns with Nonlinear control.
His most cited work include:
- A survey of deep neural network architectures and their applications (1147 citations)
- Global exponential stability of generalized recurrent neural networks with discrete and distributed delays (601 citations)
- H/sub /spl infin// control for networked systems with random communication delays (573 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Control theory, Nonlinear system, Stochastic process, Filter and Estimator.
His work is dedicated to discovering how Control theory, Upper and lower bounds are connected with Covariance and other disciplines.
His work focuses on many connections between Stochastic process and other disciplines, such as Random variable, that overlap with his field of interest in Probability distribution.
Zidong Wang has included themes like Kalman filter, Algorithm and Wireless sensor network in his Filter study.
His research integrates issues of Artificial neural network, State, Transmission, Bounded function and Complex network in his study of Estimator.
His Exponential stability study combines topics from a wide range of disciplines, such as Robust control and Stability theory.
He most often published in these fields:
- Control theory (77.95%)
- Nonlinear system (32.98%)
- Stochastic process (24.54%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (77.95%)
- Algorithm (12.46%)
- Estimator (17.64%)
In recent papers he was focusing on the following fields of study:
Zidong Wang mostly deals with Control theory, Algorithm, Estimator, Upper and lower bounds and Nonlinear system.
His work on Discrete time and continuous time, Control theory and Exponential stability is typically connected to Convex optimization as part of general Control theory study, connecting several disciplines of science.
In his study, Rate of convergence is inextricably linked to Benchmark, which falls within the broad field of Algorithm.
His work deals with themes such as Random variable, Artificial neural network, Stochastic process, State and Complex network, which intersect with Estimator.
His study on Upper and lower bounds also encompasses disciplines like
- Covariance together with Quantization,
- Filter which intersects with area such as Wireless sensor network and Transmission.
His Nonlinear system research incorporates themes from Moving horizon estimation and Markov chain.
Between 2019 and 2021, his most popular works were:
- A Set-Membership Approach to Event-Triggered Filtering for General Nonlinear Systems Over Sensor Networks (66 citations)
- Distributed Set-Membership Filtering for Multirate Systems Under the Round-Robin Scheduling Over Sensor Networks (63 citations)
- Variance-Constrained Recursive State Estimation for Time-Varying Complex Networks With Quantized Measurements and Uncertain Inner Coupling (53 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Control theory
His primary areas of study are Estimator, Control theory, Upper and lower bounds, Nonlinear system and Algorithm.
He combines subjects such as Linear system, Stochastic process, State, Symmetric matrix and Complex network with his study of Estimator.
His Stochastic process research includes themes of Bernoulli's principle and Mathematical optimization.
The concepts of his Control theory study are interwoven with issues in Artificial neural network, Transmission and Bounded function.
His studies deal with areas such as Covariance and Filter as well as Upper and lower bounds.
His work carried out in the field of Nonlinear system brings together such families of science as Control theory, Markov chain and Optimal control.
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