2023 - Research.com Computer Science in Australia Leader Award
2005 - IEEE Fellow For contributions to biologically inspired information Systems.
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.
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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Chin-Teng Lin;C. S. George Lee.
Neural-network-based fuzzy logic control and decision system
C.-T. Lin;C.S.G. Lee.
IEEE Transactions on Computers (1991)
Neural fuzzy systems
C. T. Lin.
An online self-constructing neural fuzzy inference network and its applications
Chia-Feng Juang;Chin-Teng Lin.
IEEE Transactions on Fuzzy Systems (1998)
A review of clustering techniques and developments
Amit Saxena;Mukesh Prasad;Akshansh Gupta;Neha Bharill.
EEG-based drowsiness estimation for safety driving using independent component analysis
Chin-Teng Lin;Ruei-Cheng Wu;Sheng-Fu Liang;Wen-Hung Chao.
IEEE Transactions on Circuits and Systems I-regular Papers (2005)
Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects
Omprakash Kaiwartya;Abdul Hanan Abdullah;Yue Cao;Ayman Altameem.
IEEE Access (2016)
Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems
Chin-Teng Lin;C.S.G. Lee.
IEEE Transactions on Fuzzy Systems (1994)
A recurrent self-organizing neural fuzzy inference network
Chia-Feng Juang;Chin-Teng Lin.
IEEE Transactions on Neural Networks (1999)
Novel Dry Polymer Foam Electrodes for Long-Term EEG Measurement
Chin-Teng Lin;Lun-De Liao;Yu-Hang Liu;I-Jan Wang.
IEEE Transactions on Biomedical Engineering (2011)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: