What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Electrical engineering
Ying Tan mainly investigates Mathematical optimization, Artificial intelligence, Benchmark, Control theory and Swarm intelligence.
His work deals with themes such as Stability, Convergence and Arbitrarily large, which intersect with Mathematical optimization.
Ying Tan has researched Artificial intelligence in several fields, including Algorithm, Machine learning and Pattern recognition.
His studies in Benchmark integrate themes in fields like Evolutionary computation, Global optimum, Speedup and Surrogate model.
His study connects Wavelet and Control theory.
His biological study spans a wide range of topics, including Swarm robotics, Swarm behaviour and Optimization problem.
His most cited work include:
- Fireworks algorithm for optimization (531 citations)
- On non-local stability properties of extremum seeking control (442 citations)
- Linear and Nonlinear Iterative Learning Control (316 citations)
What are the main themes of his work throughout his whole career to date?
Ying Tan focuses on Artificial intelligence, Control theory, Mathematical optimization, Algorithm and Nonlinear system.
His Artificial intelligence study integrates concerns from other disciplines, such as Swarm intelligence, Machine learning and Pattern recognition.
His is doing research in Iterative learning control, Control theory, Trajectory, Stability and Robustness, both of which are found in Control theory.
The study incorporates disciplines such as Function, Iterative method and Adaptive control in addition to Iterative learning control.
His studies deal with areas such as Convergence and Benchmark as well as Mathematical optimization.
A large part of his Nonlinear system studies is devoted to Exponential stability.
He most often published in these fields:
- Artificial intelligence (25.32%)
- Control theory (23.72%)
- Mathematical optimization (22.28%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (25.32%)
- Control theory (23.72%)
- Benchmark (10.42%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Control theory, Benchmark, Machine learning and Mathematical optimization are his primary areas of study.
Nonlinear system, Iterative learning control, Robustness, Control theory and Trajectory are the primary areas of interest in his Control theory study.
His study in Iterative learning control is interdisciplinary in nature, drawing from both Function and Convergence.
His Robustness study incorporates themes from Control system, Bounded function and Inverted pendulum.
His Benchmark research focuses on subjects like Algorithm, which are linked to Fitness landscape.
His study in Evolutionary algorithm, Optimization problem, Particle swarm optimization and Surrogate model is carried out as part of his Mathematical optimization studies.
Between 2017 and 2021, his most popular works were:
- Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics (126 citations)
- Improving Metaheuristic Algorithms With Information Feedback Models (87 citations)
- Surrogate-assisted hierarchical particle swarm optimization (84 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Electrical engineering
His primary areas of study are Benchmark, Artificial intelligence, Mathematical optimization, Control theory and Fireworks algorithm.
His work carried out in the field of Benchmark brings together such families of science as Point, Algorithm and Swarm behaviour.
His study focuses on the intersection of Algorithm and fields such as Fitness landscape with connections in the field of Convergence and Rework.
His Artificial intelligence study combines topics in areas such as Machine learning, Position and Pattern recognition.
His work on Particle swarm optimization and Evolutionary algorithm as part of his general Mathematical optimization study is frequently connected to Test suite, thereby bridging the divide between different branches of science.
His Control theory research is multidisciplinary, incorporating perspectives in Position sensor and Robot end effector.
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