What is he best known for?
The fields of study he is best known for:
- Control theory
- Artificial intelligence
- Mathematical analysis
His primary areas of study are Control theory, Stochastic neural network, Mathematical optimization, Artificial neural network and Stability theory.
In the field of Control theory, his study on Lyapunov function and Nonlinear system overlaps with subjects such as Stochastic process, Synchronization and Node.
Stochastic neural network is connected with Synchronization of chaos and Perturbation in his study.
His work in the fields of Mathematical optimization, such as Particle swarm optimization and Differential evolution, overlaps with other areas such as Population size and Robustness.
His Stability theory study frequently draws parallels with other fields, such as Exponential stability.
His research investigates the connection between Exponential stability and topics such as Linear matrix inequality that intersect with problems in Stability conditions.
His most cited work include:
- Robust stability for stochastic Hopfield neural networks with time delays (188 citations)
- Exponential stability of uncertain stochastic neural networks with mixed time-delays (170 citations)
- Synchronization of Nonlinear Dynamical Networks With Heterogeneous Impulses (140 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Control theory, Synchronization, Artificial neural network, Synchronization of chaos and Synchronization.
His Control theory study frequently draws connections between adjacent fields such as Stochastic neural network.
His Artificial neural network study integrates concerns from other disciplines, such as Exponential stability, Sequence, Time delays and Optimization problem, Mathematical optimization.
The various areas that he examines in his Exponential stability study include Robust control and Stability theory.
His Particle swarm optimization study in the realm of Mathematical optimization interacts with subjects such as Matlab toolbox.
Jian-an Fang has researched Lyapunov function in several fields, including Discrete time and continuous time and Multi-agent system.
He most often published in these fields:
- Control theory (69.23%)
- Synchronization (26.92%)
- Artificial neural network (24.04%)
What were the highlights of his more recent work (between 2014-2020)?
- Control theory (69.23%)
- Synchronization (26.92%)
- Synchronization (18.27%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Control theory, Synchronization, Synchronization, Discrete time and continuous time and Artificial neural network.
His Control theory study frequently links to related topics such as Event triggered.
His Artificial neural network research is multidisciplinary, incorporating elements of Control theory and Memristor.
His biological study spans a wide range of topics, including Exponential stability and State.
His research integrates issues of Multi-agent system and Mathematical optimization in his study of Lyapunov function.
When carried out as part of a general Synchronization of chaos research project, his work on Projective synchronization is frequently linked to work in Chaotic, Perturbation, Stochastic neural network and Stochastic process, therefore connecting diverse disciplines of study.
Between 2014 and 2020, his most popular works were:
- Finite-time synchronization of the complex dynamical network with non-derivative and derivative coupling (66 citations)
- Event-triggered output feedback H∞ control for networked Markovian jump systems with quantizations (51 citations)
- Finite-time synchronization of fractional-order memristive recurrent neural networks with discontinuous activation functions (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Mathematical analysis
Jian-an Fang focuses on Control theory, Synchronization, Synchronization networks, Topology and Variable.
Jian-an Fang interconnects Event triggered and Quantization in the investigation of issues within Control theory.
A majority of his Synchronization research is a blend of other scientific areas, such as Interval, Exponential synchronization, Synchronizing, Discrete time and continuous time and Lyapunov function.
Among his research on Synchronization networks, you can see a combination of other fields of science like Class, Vibronic coupling, Derivative, Dynamical network and Finite time.
The Topology study combines topics in areas such as Differential inclusion, Settling time and Stability theory.
Jian-an Fang integrates several fields in his works, including Variable, Synchronization of chaos, Stochastic neural network and Stochastic process.
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