What is he best known for?
The fields of study he is best known for:
- Control theory
- Mathematical analysis
- Artificial intelligence
Jun Cheng mainly investigates Control theory, Asynchronous communication, Control theory, Fuzzy logic and Artificial neural network.
His work in the fields of Control theory, such as Finite time, overlaps with other areas such as Network topology.
His work in Finite time tackles topics such as State which are related to areas like Applied mathematics.
His Control theory research is multidisciplinary, relying on both Algorithm and Hidden Markov model.
His work carried out in the field of Fuzzy logic brings together such families of science as Sequence and Inverted pendulum.
His research investigates the link between Class and topics such as Lyapunov functional that cross with problems in Markovian jump and Trajectory.
His most cited work include:
- Finite-time H∞ fuzzy control of nonlinear Markovian jump delayed systems with partly uncertain transition descriptions (142 citations)
- A Flexible Terminal Approach to Sampled-Data Exponentially Synchronization of Markovian Neural Networks With Time-Varying Delayed Signals (139 citations)
- Finite-time H∞ control for a class of Markovian jump systems with mode-dependent time-varying delays via new Lyapunov functionals. (115 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Control theory, Artificial neural network, Control theory, Asynchronous communication and Fuzzy logic.
He integrates many fields in his works, including Control theory and Dwell time.
His Artificial neural network study integrates concerns from other disciplines, such as State and Applied mathematics.
His Control theory study combines topics from a wide range of disciplines, such as Control and Actuator.
His Fuzzy logic research is multidisciplinary, incorporating perspectives in Sliding mode control and Reachability.
His Markovian jump research includes themes of Class and Event triggered.
He most often published in these fields:
- Control theory (94.63%)
- Artificial neural network (28.19%)
- Control theory (21.48%)
What were the highlights of his more recent work (between 2020-2021)?
- Control theory (94.63%)
- Asynchronous communication (20.13%)
- Control theory (21.48%)
In recent papers he was focusing on the following fields of study:
His main research concerns Control theory, Asynchronous communication, Control theory, Fuzzy logic and Hidden Markov model.
His work deals with themes such as Bounded function and Interval, which intersect with Control theory.
As part of one scientific family, Jun Cheng deals mainly with the area of Interval, narrowing it down to issues related to the Artificial neural network, and often Topology.
Jun Cheng usually deals with Control theory and limits it to topics linked to Discrete time and continuous time and Sequence and Exponential function.
Jun Cheng focuses mostly in the field of Fuzzy logic, narrowing it down to topics relating to Nonlinear system and, in certain cases, DC motor and Type.
His Hidden Markov model study incorporates themes from Algorithm, Exponential stability and Inverted pendulum.
Between 2020 and 2021, his most popular works were:
- A Fuzzy Lyapunov Function Approach to Positive L l Observer Design for Positive Fuzzy Semi-Markovian Switching Systems With Its Application (22 citations)
- Asynchronous filtering for nonhomogeneous Markov jumping systems with deception attacks (7 citations)
- Static Output Feedback Quantized Control for Fuzzy Markovian Switching Singularly Perturbed Systems with Deception Attacks (4 citations)
In his most recent research, the most cited papers focused on:
- Control theory
- Mathematical analysis
- Artificial intelligence
Jun Cheng mostly deals with Asynchronous communication, Control theory, Nonlinear system, Artificial neural network and Hidden Markov model.
His work on Control theory as part of general Control theory research is frequently linked to Phase, bridging the gap between disciplines.
His study in Nonlinear system is interdisciplinary in nature, drawing from both Linear system, Positive systems and Fuzzy logic.
His Fuzzy logic research incorporates elements of Linear programming, Taylor series, Applied mathematics and Observer.
His research in Artificial neural network intersects with topics in Quantization and Interval, Topology.
His Hidden Markov model research integrates issues from Algorithm, DC motor and Exponential stability.
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