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Dr. Wang's working areas include machine learning, data mining and computational intelligence systems for Bioinformatics and Engineering Applications. Technically, his research focus falls in subtle pattern discovery and recognition using neural networks and fuzzy systems, and in recent years he has been working towards to the development of randomized methods for neural networks, specifically, contributing to develop a brand new framework for building randomized learner models, termed as Stochatic Configuration Networks (SCNs). In contrast to the existing randomised learning algorithms for single layer feed-forward neural networks, we randomly assign the input weights and biases of the hidden nodes in the light of a supervisory mechanism, and the output weights are analytically evaluated in either constructive or selective manner. As fundamentals of SCN-based data modelling techniques, we establish some theoretical results on the universal approximation property. Some experimental results indicate that our proposed SCNs outperform than other randomized neural networks in terms of less human intervention on the network size setting, the scope adaptation of random parameters, fast learning and sound generalization. Deep sctochastic configuration networks (DeepSCNs) have been developed and mathematically proved as universal approximators for continous nonlinear functions defined over compact sets. DeepSCNs can be constructed efficiently (much faster than other deep neural networks) and share many great features, such as learning representation and consistency property between learning and generalization.
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Junqi Li,Dianhui Wang
Information Sciencespp.120497, (2024)
Yongxuan Chen,Dianhui Wang
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICSno. 99 (2024): 1-10
IEEE Trans. Fuzzy Syst.no. 3 (2024): 948-957
INFORMATION SCIENCES (2024): 120098
Engineering Applications of Artificial Intelligence (2024): 107833
Neural Comput. Appl.no. 28 (2023): 21229-21245
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CoRR (2023)
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OPTICAL ENGINEERINGno. 11 (2023)
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