What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mathematical analysis
- Algorithm
His primary scientific interests are in Artificial intelligence, Pattern recognition, Support vector machine, Control theory and Linear system.
When carried out as part of a general Artificial intelligence research project, his work on Probabilistic logic, Relevance vector machine, Feature and Feature extraction is frequently linked to work in Electroencephalography, therefore connecting diverse disciplines of study.
His Pattern recognition study integrates concerns from other disciplines, such as Error detection and correction, Benchmark and Artifact.
The Support vector machine study which covers Artificial neural network that intersects with Estimation theory, Exponential stability and Upper and lower bounds.
His research in Control theory is mostly concerned with Overhead crane.
His Linear system research is multidisciplinary, relying on both Dynamic programming, Bounded function, Applied mathematics and Nonlinear system.
His most cited work include:
- Spiral microchannel with rectangular and trapezoidal cross-sections for size based particle separation (177 citations)
- Parallel sequential minimal optimization for the training of support vector machines (146 citations)
- Automatic EEG Artifact Removal: A Weighted Support Vector Machine Approach With Error Correction (112 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Control theory, Linear system, Mathematical optimization, Model predictive control and Support vector machine.
His research in Control theory tackles topics such as Bounded function which are related to areas like Domain and Robust control.
The various areas that Chong Jin Ong examines in his Linear system study include Numerical stability, Computation, Invariant and Applied mathematics.
His Mathematical optimization research is multidisciplinary, incorporating perspectives in Stability and Function.
His Support vector machine research incorporates elements of Artificial neural network and Data set.
His Pattern recognition research is multidisciplinary, incorporating elements of Probabilistic logic and Data mining.
He most often published in these fields:
- Control theory (43.75%)
- Linear system (30.56%)
- Mathematical optimization (31.94%)
What were the highlights of his more recent work (between 2015-2021)?
- Linear system (30.56%)
- Mathematical optimization (31.94%)
- Nonlinear system (13.19%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Linear system, Mathematical optimization, Nonlinear system, Applied mathematics and Control theory.
His research in Linear system intersects with topics in Algorithm, Computation and Discrete time and continuous time.
His Discrete time and continuous time study combines topics from a wide range of disciplines, such as Distributed model predictive control and Optimal control.
His work in the fields of Mathematical optimization, such as Optimization problem, intersects with other areas such as Constraint.
In Optimization problem, Chong Jin Ong works on issues like Exponential stability, which are connected to Model predictive control, Nonlinear control and Stability theory.
With his scientific publications, his incorporates both Control theory and Transient.
Between 2015 and 2021, his most popular works were:
- Distributed Model Predictive Control of linear discrete-time systems with local and global constraints (31 citations)
- Adaptive neural network control of uncertain MIMO nonlinear systems with input saturation (15 citations)
- Model Predictive Control for Switching Systems With Dwell-Time Restriction (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mathematical analysis
- Algorithm
Chong Jin Ong mostly deals with Linear system, Control theory, Computation, Optimization problem and Mathematical optimization.
His Linear system research incorporates themes from Linear programming, Algorithm and Strongly connected component.
His biological study spans a wide range of topics, including Artificial neural network and Tracking.
His Computation study integrates concerns from other disciplines, such as Quadratic equation, Class, Invariant, Applied mathematics and Linear matrix.
His studies in Optimization problem integrate themes in fields like Discrete time and continuous time, Exponential stability and Model predictive control.
His Discrete time and continuous time research is multidisciplinary, incorporating perspectives in Distributed model predictive control and Optimal control.
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