World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
43
Citations
8985
World Ranking
7902
National Ranking
249

Overview

Dianhui Wang is affiliated with La Trobe University in Australia and has an extensive research portfolio primarily in the fields of Computer Science and Engineering. Their scholarly work spans topics such as Artificial Intelligence, Control and Systems Engineering, and Computer Vision and Pattern Recognition, with additional contributions in Electrical and Electronic Engineering and Mechanical Engineering.

Their main areas of study include:

  • Artificial Intelligence
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Mechanical Engineering

Dianhui Wang's research heavily focuses on Machine Learning and Extreme Learning Machines (ELM), Neural Networks and Applications, Fault Detection and Control Systems, Face and Expression Recognition, Fuzzy Logic and Control Systems, Advanced Neural Network Applications, and Advanced Graph Neural Networks.

The main topics of their work encompass:

  • Machine Learning and ELM
  • Neural Networks and Applications
  • Fault Detection and Control Systems
  • Face and Expression Recognition
  • Fuzzy Logic and Control Systems
  • Advanced Neural Network Applications
  • Advanced Graph Neural Networks

The scientist has published research in frequently appearing venues such as:

  • Information Sciences
  • Neural Computing and Applications
  • IEEE Transactions on Fuzzy Systems
  • IEEE Transactions on Industrial Informatics
  • arXiv (Cornell University)

Among significant recent papers authored or co-authored by Dianhui Wang are:

  • "Effective Deep Attributed Network Representation Learning With Topology Adapted Smoothing" (2021), IEEE Transactions on Cybernetics
  • "Fuzzy Stochastic Configuration Networks for Nonlinear System Modeling" (2023), IEEE Transactions on Fuzzy Systems
  • "Prediction of component concentrations in sodium aluminate liquor using stochastic configuration networks" (2020), Neural Computing and Applications
  • "Online Self-Learning Stochastic Configuration Networks for Nonstationary Data Stream Analysis" (2023), IEEE Transactions on Industrial Informatics
  • "Stochastic configuration network ensembles with selective base models" (2021), Neural Networks

Frequent co-authors working with Dianhui Wang include:

  • Gang Dang
  • Yongxuan Chen
  • Aijun Yan
  • Pengxin Tian
  • Matthew J. Felicetti

Their contributions mainly address advanced methodologies in neural networks, stochastic configuration networks, and fuzzy systems, with applications ranging from system modeling and data stream analysis to network representation learning.

Best Publications

  • Extreme learning machines: a survey

    Guang-Bin Huang;Dian Hui Wang;Yuan Lan

  • Stochastic Configuration Networks: Fundamentals and Algorithms

    Dianhui Wang;Ming Li

  • Randomness in neural networks: an overview

    Simone Scardapane;Dianhui Wang

  • Fast decorrelated neural network ensembles with random weights

    Monther Alhamdoosh;Dianhui Wang

  • Insights into randomized algorithms for neural networks: Practical issues and common pitfalls

    Ming Li;Dianhui Wang

  • A Self-Adaptive RBF Neural Network Classifier for Transformer Fault Analysis

    Ke Meng;Zhao Yang Dong;Dian Hui Wang;Kit Po Wong

  • Assessing Short-Term Voltage Stability of Electric Power Systems by a Hierarchical Intelligent System

    Yan Xu;Rui Zhang;Junhua Zhao;Zhao Yang Dong

  • Distributed learning for Random Vector Functional-Link networks

    Simone Scardapane;Dianhui Wang;Massimo Panella;Aurelio Uncini

  • Robust stochastic configuration networks with kernel density estimation for uncertain data regression

    Dianhui Wang;Ming Li

  • Protein sequence classification using extreme learning machine

    Dianhui Wang;Guang-Bin Huang

  • Stochastic configuration networks ensemble with heterogeneous features for large-scale data analytics

    Dianhui Wang;Caihao Cui

  • A robust adaptive neural networks controller for maritime dynamic positioning system

    Jialu Du;Yang Yang;Dianhui Wang;Chen Guo

  • A decentralized training algorithm for Echo State Networks in distributed big data applications

    Simone Scardapane;Dianhui Wang;Massimo Panella

  • New Stability Criteria of Delayed Load Frequency Control Systems via Infinite-Series-Based Inequality

    Feisheng Yang;Jing He;Dianhui Wang

  • Deep Stochastic Configuration Networks with Universal Approximation Property

    Dianhui Wang;Ming Li

  • Global Convergence of Online BP Training With Dynamic Learning Rate

    Rui Zhang;Zong-Ben Xu;Guang-Bin Huang;Dianhui Wang

  • 2-D Stochastic Configuration Networks for Image Data Analytics

    Ming Li;Dianhui Wang

  • Adaptive robust control of oxygen excess ratio for PEMFC system based on type-2 fuzzy logic system

    H. K. Zhang;Y. F. Wang;Dianhui Wang;Dianhui Wang;Y. L. Wang

  • Flame Image-Based Burning State Recognition for Sintering Process of Rotary Kiln Using Heterogeneous Features and Fuzzy Integral

    Weitao Li;Dianhui Wang;Tianyou Chai

  • Evolutionary extreme learning machine ensembles with size control

    Dianhui Wang;Monther Alhamdoosh

  • A new robust training algorithm for a class of single-hidden layer feedforward neural networks

    Zhihong Man;Kevin Lee;Dianhui Wang;Zhenwei Cao

  • Letters: A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome

    Joon Shik Lim;Dianhui Wang;Yong-Soo Kim;Sudhir Gupta

Frequent Co-Authors

Tharam S. Dillon
Tharam S. Dillon La Trobe University
Tianyou Chai
Tianyou Chai Northeastern University
Zhihong Man
Zhihong Man Swinburne University of Technology
Elizabeth Chang
Elizabeth Chang Griffith University
Zhenwei Cao
Zhenwei Cao Swinburne University of Technology
Guang-Bin Huang
Guang-Bin Huang Nanyang Technological University
Mahardhika Pratama
Mahardhika Pratama University of South Australia
Witold Pedrycz
Witold Pedrycz University of Alberta
Zhao Yang Dong
Zhao Yang Dong City University of Hong Kong
Junhua Zhao
Junhua Zhao Chinese University of Hong Kong, Shenzhen

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