Dianhui Wang

Reader in Computational Intelligence

La Trobe University

Australia

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Dianhui Wang is a Reader in Computational Intelligence at La Trobe University, Australia. His research focuses on the development of advanced modeling techniques for uncertain and nonstationary data, as evidenced by his recent articles on Granular Stochastic Configuration Networks and Fuzzy Recurrent Stochastic Configuration Networks. His work contributes significantly to industrial data modeling and analytics, as well as the theoretical foundations of stochastic configuration networks.

Articles (24)

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2025

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2022

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2022

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2022

Collaborators (2)

Gaoxi Xiao

Nanyang Technological University (NTU)

SINGAPORE

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Ivan Tyukin

Professor of Mathematical Data Science and Modelling

King’s College London

UNITED KINGDOM

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Dianhui Wang

Research Interests

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About

Articles (24)

Granular stochastic configuration networks for uncertain data modeling

Fuzzy Kernel Stochastic Configuration Networks for Industrial Data Modeling

Online Self-Learning Fuzzy Recurrent Stochastic Configuration Networks for Modeling Nonstationary Dynamics

An Improved Fuzzy Recurrent Stochastic Configuration Network for Modeling Nonlinear Systems

Fuzzy Recurrent Stochastic Configuration Networks for Industrial Data Analytics

Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systems

Kernel Stochastic Configuration Networks for Industrial Data Regression

Monitoring Model Based on Data-Driven Optimization Stochastic Configuration Network and Its Applications

Self-Organizing Recurrent Stochastic Configuration Networks for Nonstationary Data Modeling

Theoretical Advances on Stochastic Configuration Networks

An empirical study on prediction of seismic activity using stochastic configuration networks

A Deep Nonconnectionist Learning Framework for Industrial Data Modeling

An Improved Deep Kernel Partial Least Squares and Its Application to Industrial Data Modeling

Predicting Particle Size of Copper Ore Grinding With Stochastic Configuration Networks

Activity recognition in rehabilitation training based on ensemble stochastic configuration networks

Attention-Based Multi-Agent Graph Reinforcement Learning for Service Restoration

Event-Triggered Prespecified Performance Control for Steer-by-Wire Systems With Input Nonlinearity

Fuzzy Stochastic Configuration Networks for Nonlinear System Modeling

Online Self-Learning Stochastic Configuration Networks for Nonstationary Data Stream Analysis

Industrial data classification using stochastic configuration networks with self-attention learning features

Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process

Performance of soft sensors based on stochastic configuration networks with nonnegative garrote

Effective Deep Attributed Network Representation Learning With Topology Adapted Smoothing

Multiview Spectral Clustering via Robust Subspace Segmentation

Collaborators (2)

Gaoxi Xiao

Ivan Tyukin

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