What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Control theory, Nonlinear system, Adaptive control, Backstepping and Artificial neural network.
Control theory is closely attributed to Fuzzy logic in his work.
His Nonlinear system research is multidisciplinary, relying on both Multi-agent system, Bounded function and Cryptography.
As part of one scientific family, C. L. Philip Chen deals mainly with the area of Adaptive control, narrowing it down to issues related to the State, and often Trajectory, Torque, Type, Stability and Constraint.
His studies deal with areas such as Observer, Tracking error and Adaptive neuro fuzzy inference system as well as Backstepping.
C. L. Philip Chen combines subjects such as Incremental learning, MIMO and Inference with his study of Artificial neural network.
His most cited work include:
- Data-intensive applications, challenges, techniques and technologies: A survey on Big Data (1617 citations)
- A new 1D chaotic system for image encryption (417 citations)
- Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture (381 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Control theory, Artificial intelligence, Nonlinear system, Pattern recognition and Artificial neural network.
Control theory is frequently linked to Fuzzy logic in his study.
The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence.
His research in Nonlinear system intersects with topics in Multi-agent system, Bounded function, Actuator and Adaptive system.
His Pattern recognition research includes themes of Pixel and Feature.
His Artificial neural network study which covers Robustness that intersects with Outlier.
He most often published in these fields:
- Control theory (43.69%)
- Artificial intelligence (37.85%)
- Nonlinear system (35.69%)
What were the highlights of his more recent work (between 2019-2021)?
- Control theory (43.69%)
- Nonlinear system (35.69%)
- Artificial intelligence (37.85%)
In recent papers he was focusing on the following fields of study:
C. L. Philip Chen mostly deals with Control theory, Nonlinear system, Artificial intelligence, Artificial neural network and Control theory.
His work on Control theory is being expanded to include thematically relevant topics such as Fuzzy logic.
His study on Nonlinear system also encompasses disciplines like
- Bounded function which intersects with area such as State,
- Multi-agent system, which have a strong connection to Control.
The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His Artificial neural network research is multidisciplinary, incorporating elements of Control system, Algorithm, Estimator and Robot.
Outlier is closely connected to Robustness in his research, which is encompassed under the umbrella topic of Control theory.
Between 2019 and 2021, his most popular works were:
- Integral Barrier Lyapunov function-based adaptive control for switched nonlinear systems (59 citations)
- Integral Barrier Lyapunov function-based adaptive control for switched nonlinear systems (59 citations)
- Fuzzy Broad Learning System: A Novel Neuro-Fuzzy Model for Regression and Classification (54 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
C. L. Philip Chen spends much of his time researching Control theory, Nonlinear system, Artificial neural network, Control theory and Artificial intelligence.
His Control theory and Lyapunov function, Backstepping, Adaptive control, Stability and State observer investigations all form part of his Control theory research activities.
His Nonlinear system study integrates concerns from other disciplines, such as Bounded function, Actuator, Adaptive system and Fuzzy control system.
His Artificial neural network study incorporates themes from Feature, Multi-agent system, Trajectory optimization, Algorithm and Robustness.
His studies deal with areas such as Reduced order, Markov process, Trajectory and Robot manipulator as well as Control theory.
The study incorporates disciplines such as Invariant, Computer vision and Pattern recognition in addition to Artificial intelligence.
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