What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Control theory
- Algorithm
The scientist’s investigation covers issues in Artificial neural network, Recurrent neural network, Control theory, Mathematical optimization and Quadratic programming.
The study incorporates disciplines such as Computation and Robustness in addition to Artificial neural network.
His Recurrent neural network research incorporates themes from Control system, Stability, Control, Nonlinear system and Activation function.
His biological study spans a wide range of topics, including Winner-take-all and Topology.
His work carried out in the field of Control theory brings together such families of science as Convergence and Robotic arm.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Function and Monotonic function.
His most cited work include:
- A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method (181 citations)
- Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks (179 citations)
- Accelerating a Recurrent Neural Network to Finite-Time Convergence for Solving Time-Varying Sylvester Equation by Using a Sign-Bi-power Activation Function (168 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, Robot, Recurrent neural network and Artificial intelligence.
In general Control theory, his work in Nonlinear system, Control theory and Optimal control is often linked to Redundancy linking many areas of study.
His study focuses on the intersection of Artificial neural network and fields such as Mathematical optimization with connections in the field of Algorithm.
His studies deal with areas such as Control engineering, Control, Task and Trajectory as well as Robot.
Recurrent neural network is closely attributed to Stability in his work.
His research in Artificial intelligence intersects with topics in Machine learning, Computer vision and Pattern recognition.
He most often published in these fields:
- Control theory (36.46%)
- Artificial neural network (26.30%)
- Robot (22.66%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (36.46%)
- Robot (22.66%)
- Artificial neural network (26.30%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Control theory, Robot, Artificial neural network, Recurrent neural network and Nonlinear system.
Shuai Li combines subjects such as Convergence and Quadratic programming with his study of Control theory.
His Convergence research includes elements of Jacobian matrix and determinant and Motion control.
His Robot study incorporates themes from Task, Trajectory and Identification.
His Recurrent neural network study frequently intersects with other fields, such as Stability.
The concepts of his Nonlinear system study are interwoven with issues in Mathematical optimization and Optimal control.
Between 2019 and 2021, his most popular works were:
- Adaptive Neural Output-Feedback Decentralized Control for Large-Scale Nonlinear Systems With Stochastic Disturbances (62 citations)
- Non-Negativity Constrained Missing Data Estimation for High-Dimensional and Sparse Matrices from Industrial Applications (39 citations)
- Obstacle Avoidance and Tracking Control of Redundant Robotic Manipulator: An RNN-Based Metaheuristic Approach (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Control theory
- Algorithm
His primary areas of study are Control theory, Artificial neural network, Robot, Robustness and Motion planning.
His research integrates issues of Recurrent neural network and Quadratic programming in his study of Control theory.
Shuai Li has researched Recurrent neural network in several fields, including Stability, Control theory, Trajectory and Match moving.
His studies deal with areas such as Discretization, Convergence and Linear system as well as Artificial neural network.
His work deals with themes such as Control engineering and Feature extraction, which intersect with Robot.
The Motion planning study combines topics in areas such as Search algorithm and Motion control.
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