What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Chin-Teng Lin mostly deals with Artificial intelligence, Electroencephalography, Artificial neural network, Fuzzy logic and Machine learning.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Computer vision and Pattern recognition.
His Electroencephalography research is multidisciplinary, relying on both Speech recognition, Independent component analysis, Cognition and Simulation, Driving simulator.
His Artificial neural network research incorporates elements of Nonlinear system, Control theory, Feed forward and Genetic algorithm.
His Fuzzy logic study incorporates themes from Control system and Gradient descent.
His research in Neuro-fuzzy tackles topics such as Fuzzy set operations which are related to areas like Fuzzy classification.
His most cited work include:
- Neural-network-based fuzzy logic control and decision system (1323 citations)
- Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems (1243 citations)
- An online self-constructing neural fuzzy inference network and its applications (837 citations)
What are the main themes of his work throughout his whole career to date?
Chin-Teng Lin mainly investigates Artificial intelligence, Electroencephalography, Pattern recognition, Fuzzy logic and Artificial neural network.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Computer vision.
His Electroencephalography research incorporates themes from Speech recognition, Simulation, Cognition and Independent component analysis.
Chin-Teng Lin interconnects Robustness and Cluster analysis in the investigation of issues within Pattern recognition.
His biological study spans a wide range of topics, including Control system, Control theory and Particle swarm optimization.
The various areas that he examines in his Neuro-fuzzy study include Fuzzy number, Defuzzification, Fuzzy rule and Adaptive neuro fuzzy inference system.
He most often published in these fields:
- Artificial intelligence (49.08%)
- Electroencephalography (30.08%)
- Pattern recognition (19.66%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (49.08%)
- Electroencephalography (30.08%)
- Brain–computer interface (11.48%)
In recent papers he was focusing on the following fields of study:
Chin-Teng Lin mainly focuses on Artificial intelligence, Electroencephalography, Brain–computer interface, Pattern recognition and Machine learning.
His studies link Computer vision with Artificial intelligence.
The Electroencephalography study combines topics in areas such as Cognitive psychology, Stimulus, Speech recognition, Task and Cognition.
His research integrates issues of Adversarial system, Linear subspace and Human–computer interaction in his study of Brain–computer interface.
His Pattern recognition research includes elements of Cluster analysis and Fuzzy logic.
His research in Artificial neural network intersects with topics in Generalization, Fuzzy control system and Big data.
Between 2018 and 2021, his most popular works were:
- Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach (60 citations)
- Multi-channel EEG recordings during a sustained-attention driving task. (60 citations)
- An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making (44 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Electrical engineering
The scientist’s investigation covers issues in Artificial intelligence, Electroencephalography, Brain–computer interface, Pattern recognition and Machine learning.
Artificial intelligence and Computer vision are frequently intertwined in his study.
The study incorporates disciplines such as Brain control, Sustaining attention, Cognitive psychology, Speech recognition and Forehead in addition to Electroencephalography.
His Brain–computer interface research is multidisciplinary, incorporating elements of Adversarial system, Visualization, Task analysis and Human–computer interaction.
His research in Pattern recognition intersects with topics in Stimulus, Resting state fMRI and Entropy.
His study in Machine learning is interdisciplinary in nature, drawing from both Perspective and Fuzzy control system.
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