What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Artificial neural network
- Control theory
Zhigang Zeng spends much of his time researching Control theory, Artificial neural network, Exponential stability, Recurrent neural network and Memristor.
His work on Stability as part of general Control theory research is frequently linked to Synchronization, thereby connecting diverse disciplines of science.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Landslide, Control theory and Hybrid system.
His Exponential stability research is multidisciplinary, relying on both Equilibrium point, Cellular neural network, Bounded function, Applied mathematics and Stability conditions.
His studies in Recurrent neural network integrate themes in fields like Time delays and Content-addressable memory.
The study incorporates disciplines such as Differential inclusion, Class, Adaptive control and Differential equation in addition to Memristor.
His most cited work include:
- Global Mittag-Leffler stability and synchronization of memristor-based fractional-order neural networks (351 citations)
- Synchronization control of a class of memristor-based recurrent neural networks (318 citations)
- Exponential Stabilization of Memristive Neural Networks With Time Delays (225 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial neural network, Control theory, Exponential stability, Memristor and Recurrent neural network.
Zhigang Zeng works mostly in the field of Artificial neural network, limiting it down to topics relating to Applied mathematics and, in certain cases, Type, as a part of the same area of interest.
The various areas that Zhigang Zeng examines in his Control theory study include Bounded function and Stability.
His Exponential stability study also includes
- Equilibrium point which connect with Multistability,
- Cellular neural network which is related to area like Algorithm.
The Memristor study combines topics in areas such as Differential inclusion, Neuromorphic engineering, Synapse and Topology.
His study in Recurrent neural network is interdisciplinary in nature, drawing from both Activation function and Content-addressable memory.
He most often published in these fields:
- Artificial neural network (54.33%)
- Control theory (48.56%)
- Exponential stability (24.28%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial neural network (54.33%)
- Control theory (48.56%)
- Synchronization (9.38%)
In recent papers he was focusing on the following fields of study:
Artificial neural network, Control theory, Synchronization, Topology and Synchronization are his primary areas of study.
His Artificial neural network study combines topics from a wide range of disciplines, such as Exponential stability, Lyapunov function, Applied mathematics and Memristor.
His work deals with themes such as Initial value problem, Computer engineering, Content-addressable memory, Artificial intelligence and Synapse, which intersect with Memristor.
As part of his studies on Control theory, Zhigang Zeng often connects relevant subjects like Bounded function.
His Synchronization research is multidisciplinary, incorporating elements of Mode, Differential inequalities, Stability theory, Time delays and Computer simulation.
In the subject of general Topology, his work in Attractor is often linked to Network topology, thereby combining diverse domains of study.
Between 2019 and 2021, his most popular works were:
- Memristor-Based Neural Network Circuit of Full-Function Pavlov Associative Memory With Time Delay and Variable Learning Rate (30 citations)
- Memristive LSTM Network for Sentiment Analysis (28 citations)
- Lagrange Stability and Finite-Time Stabilization of Fuzzy Memristive Neural Networks With Hybrid Time-Varying Delays (23 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Artificial neural network
- Mathematical analysis
Zhigang Zeng mainly investigates Artificial neural network, Control theory, Exponential stability, Synchronization and Memristor.
When carried out as part of a general Artificial neural network research project, his work on Recurrent neural network and Activation function is frequently linked to work in Inertial frame of reference and Synchronization, therefore connecting diverse disciplines of study.
Zhigang Zeng has included themes like Discrete mathematics, Equilibrium point, Piecewise linear function and Bounding overwatch in his Recurrent neural network study.
Zhigang Zeng has researched Control theory in several fields, including Bounded function and Fuzzy logic.
His Exponential stability research is multidisciplinary, incorporating perspectives in Algebraic number, Numerical stability, Pseudorandom number generator, Applied mathematics and Differential inclusion.
His biological study spans a wide range of topics, including Synapse, Content-addressable memory, Computer engineering and Arithmetic.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
