Meng Joo Er mostly deals with Fuzzy logic, Artificial intelligence, Control theory, Fuzzy control system and Neuro-fuzzy. His Fuzzy logic research is multidisciplinary, incorporating perspectives in Mathematical optimization and Backstepping. His Artificial intelligence study combines topics in areas such as Machine learning, Nonlinear system and Pattern recognition.
His Machine learning research includes elements of Fuzzy classification and Data mining. The various areas that Meng Joo Er examines in his Fuzzy control system study include Tracking error, Open-loop controller and PID controller. His work focuses on many connections between Neuro-fuzzy and other disciplines, such as Adaptive neuro fuzzy inference system, that overlap with his field of interest in Defuzzification, Mobile robot and Reinforcement learning.
Meng Joo Er focuses on Control theory, Artificial intelligence, Fuzzy logic, Fuzzy control system and Artificial neural network. His research in Control theory tackles topics such as Control engineering which are related to areas like Robot and Stability. Meng Joo Er combines subjects such as Machine learning, Computer vision and Pattern recognition with his study of Artificial intelligence.
Meng Joo Er studies Neuro-fuzzy, a branch of Fuzzy logic. His Fuzzy control system study integrates concerns from other disciplines, such as Tracking error and Exponential stability. In Artificial neural network, Meng Joo Er works on issues like Pruning, which are connected to Extended Kalman filter.
Meng Joo Er spends much of his time researching Control theory, Artificial intelligence, Fuzzy logic, Nonlinear system and Trajectory. His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Data mining, Computer vision and Pattern recognition. Meng Joo Er specializes in Machine learning, namely Artificial neural network.
His Fuzzy logic research incorporates themes from Lyapunov function and Interpolation. His study in the field of Adaptive control is also linked to topics like Class. In general Fuzzy control system study, his work on Adaptive neuro fuzzy inference system and Neuro-fuzzy often relates to the realm of Pyramid, thereby connecting several areas of interest.
His primary scientific interests are in Control theory, Fuzzy logic, Artificial intelligence, Fuzzy control system and Artificial neural network. His study in Nonlinear system, Vehicle dynamics, Adaptive control, Robustness and Trajectory is carried out as part of his Control theory studies. His Fuzzy logic research is multidisciplinary, incorporating elements of Control system and Backstepping.
The Artificial intelligence study combines topics in areas such as Machine learning and Computer vision. As a part of the same scientific study, Meng Joo Er usually deals with the Machine learning, concentrating on Data mining and frequently concerns with Fuzzy clustering, Unsupervised learning and Mode. His work carried out in the field of Fuzzy control system brings together such families of science as Tracking error, Proportional derivative and Lyapunov function.
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Face recognition with radial basis function (RBF) neural networks
Meng Joo Er;Shiqian Wu;Juwei Lu;Hock Lye Toh.
IEEE Transactions on Neural Networks (2002)
Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain
W. Chen;Meng Joo Er;Shiqian Wu.
systems man and cybernetics (2006)
A review of clustering techniques and developments
Amit Saxena;Mukesh Prasad;Akshansh Gupta;Neha Bharill.
Dynamic fuzzy neural networks-a novel approach to function approximation
Shiqian Wu;Meng Joo Er.
systems man and cybernetics (2000)
A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
Shiqian Wu;Meng Joo Er;Yang Gao.
IEEE Transactions on Fuzzy Systems (2001)
High-speed face recognition based on discrete cosine transform and RBF neural networks
Meng Joo Er;W. Chen;Shiqian Wu.
IEEE Transactions on Neural Networks (2005)
NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches
Yang Gao;Meng Joo Er.
Fuzzy Sets and Systems (2005)
Wireless Sensor Networks for Industrial Environments
K.S. Low;W.N.N. Win;M.J. Er.
computational intelligence for modelling, control and automation (2005)
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
Yang Gao;Meng Joo Er.
IEEE Transactions on Fuzzy Systems (2003)
A hybrid particle swarm optimization based fuzzy expert system for the diagnosis of coronary artery disease
S. Muthukaruppan;M. J. Er.
Expert Systems With Applications (2012)
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