M
Meng Joo Er
Researcher at Dalian Maritime University
Publications - 428
Citations - 11918
Meng Joo Er is an academic researcher from Dalian Maritime University. The author has contributed to research in topics: Fuzzy logic & Fuzzy control system. The author has an hindex of 50, co-authored 424 publications receiving 10192 citations. Previous affiliations of Meng Joo Er include Wuhan University & Singapore Polytechnic.
Papers
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Journal ArticleDOI
A review of clustering techniques and developments
Amit Saxena,Mukesh Prasad,Akshansh Gupta,Neha Bharill,Om Prakash Patel,Aruna Tiwari,Meng Joo Er,Weiping Ding,Chin-Teng Lin +8 more
TL;DR: The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted and the approaches used in these methods are discussed with their respective states of art and applicability.
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Face recognition with radial basis function (RBF) neural networks
TL;DR: A novel paradigm is proposed whereby data information is encapsulated in determining the structure and initial parameters of the RBF neural classifier before learning takes place, and the dimension of the search space is drastically reduced in the gradient paradigm.
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Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain
W. Chen,Meng Joo Er,Shiqian Wu +2 more
TL;DR: A novel illumination normalization approach for face recognition under varying lighting conditions using a discrete cosine transform to compensate for illumination variations in the logarithm domain that is easily implemented in a real-time face recognition system.
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Dynamic fuzzy neural networks-a novel approach to function approximation
Shiqian Wu,Meng Joo Er +1 more
TL;DR: Simulation studies and comprehensive comparisons with some other learning algorithms demonstrate that a more compact structure with higher performance can be achieved by the proposed approach.
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A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
Shiqian Wu,Meng Joo Er,Yang Gao +2 more
TL;DR: Comprehensive comparisons with other latest approaches show that the proposed approach is superior in terms of learning efficiency and performance.