What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Control theory
His primary areas of investigation include Control theory, Batch processing, Iterative learning control, Control theory and Control engineering.
Control theory and Mathematical optimization are frequently intertwined in his study.
Iterative learning control is closely attributed to Interval in his study.
The various areas that Furong Gao examines in his Control theory study include Stability, Process control and Molding.
The study incorporates disciplines such as Control and Setpoint in addition to Control engineering.
His work deals with themes such as Process modeling, Algorithm, Nonlinear system and Fault detection and isolation, which intersect with Principal component analysis.
His most cited work include:
- Review of Recent Research on Data-Based Process Monitoring (534 citations)
- Survey on iterative learning control, repetitive control, and run-to-run control (490 citations)
- Sub‐PCA modeling and on‐line monitoring strategy for batch processes (193 citations)
What are the main themes of his work throughout his whole career to date?
Furong Gao mainly focuses on Control theory, Batch processing, Iterative learning control, Control theory and Molding.
The Control theory study combines topics in areas such as Control engineering, Process control and Model predictive control.
His Control engineering research integrates issues from Control and Robustness.
The concepts of his Iterative learning control study are interwoven with issues in Fault tolerance, Stability, Mathematical optimization and Interval.
Furong Gao has researched Molding in several fields, including Mechanical engineering and Mold.
In his work, Artificial intelligence is strongly intertwined with Fault, which is a subfield of Data mining.
He most often published in these fields:
- Control theory (42.05%)
- Batch processing (21.94%)
- Iterative learning control (18.28%)
What were the highlights of his more recent work (between 2016-2021)?
- Control theory (42.05%)
- Iterative learning control (18.28%)
- Nonlinear system (12.43%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Control theory, Iterative learning control, Nonlinear system, Batch processing and Model predictive control.
Furong Gao has included themes like Fault tolerance and Control in his Control theory study.
Furong Gao studied Control and Control engineering that intersect with Process control.
His Iterative learning control study combines topics from a wide range of disciplines, such as Stability, Upper and lower bounds, Exponential stability and State.
His Nonlinear system research is multidisciplinary, incorporating perspectives in Artificial neural network, Mathematical optimization and Trajectory.
His Model predictive control research includes themes of State-space representation and State space.
Between 2016 and 2021, his most popular works were:
- Critical-to-Fault-Degradation Variable Analysis and Direction Extraction for Online Fault Prognostic (77 citations)
- A fast estimation algorithm for lithium-ion battery state of health (76 citations)
- A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging (67 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Control theory
Furong Gao mostly deals with Control theory, Batch processing, Iterative learning control, Fault detection and isolation and Battery.
His research on Control theory frequently connects to adjacent areas such as State.
His study on Batch processing is intertwined with other disciplines of science such as Residual and Scheme.
His work carried out in the field of Iterative learning control brings together such families of science as Fault tolerance, Upper and lower bounds and Fuzzy logic.
His research in Fault detection and isolation intersects with topics in Algorithm, Principal component analysis and Data mining.
His Data mining study integrates concerns from other disciplines, such as Quality, Selection, Partial least squares regression, Key and Process.
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