World's Best Scientists 2026 revealed!

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Computer Science

D-Index
52
Citations
9925
World Ranking
5122
National Ranking
36

Overview

Edwin Lughofer is affiliated with Johannes Kepler University of Linz in Austria. Their research spans multiple fields within computer science and engineering, with a focus on areas involving artificial intelligence and control systems.

Their scholarly contributions include numerous publications in prominent venues such as:

  • Information Sciences
  • arXiv (Cornell University)
  • Fuzzy Sets and Systems
  • Soft Computing
  • Applied Soft Computing

The primary fields of study in which Lughofer is active are computer science and engineering. Within these fields, their work extensively covers these subfields:

  • Artificial Intelligence
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Management Science and Operations Research
  • Biomedical Engineering

Lughofer's research topics primarily involve:

  • Fuzzy Logic and Control Systems
  • Data Stream Mining Techniques
  • Neural Networks and Applications
  • Fault Detection and Control Systems
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Machine Learning and Extreme Learning Machines (ELM)

Frequent collaborators include:

  • Paulo Vitor de Campos Souza
  • Mahardhika Pratama
  • Mu-Yen Chen
  • Augusto Júnio Guimarães
  • Igor Škrjanc

Selected recent publications by Edwin Lughofer include:

  • DEVDAN: deep evolving denoising autoencoder, 2020, UniSA Research Outputs Repository (University of South Australia)
  • Adapting H-infinity controller for the desired reference tracking of the sphere position in the maglev process, 2021, Information Sciences
  • PAC: a novel self-adaptive neuro-fuzzy controller for micro aerial vehicles, 2020, UniSA Research Outputs Repository (University of South Australia)
  • New aggregation operators on group-based generalized intuitionistic fuzzy soft sets, 2021, Soft Computing
  • An evolving neuro-fuzzy system based on uni-nullneurons with advanced interpretability capabilities, 2021, Neurocomputing

Best Publications

  • Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications

    Edwin Lughofer

  • Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning

    Werner Zellinger;Thomas Grubinger;Edwin Lughofer;Thomas Natschläger

  • FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

    E.D. Lughofer

  • Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

    Igor Skrjanc;José Antonio Iglesias;Araceli Sanchis;Daniel F. Leite

  • PANFIS: A Novel Incremental Learning Machine

    Mahardhika Pratama;Sreenatha G. Anavatti;Plamen P. Angelov;Edwin Lughofer

  • Evolving fuzzy classifiers using different model architectures

    P. Angelov;E. Lughofer;X. Zhou

  • Handling drifts and shifts in on-line data streams with evolving fuzzy systems

    E. Lughofer;P. Angelov

  • Extensions of vector quantization for incremental clustering

    Edwin Lughofer

  • GENEFIS: Toward an Effective Localist Network

    Mahardhika Pratama;Sreenatha G. Anavatti;Edwin Lughofer

  • Learning in Non-Stationary Environments: Methods and Applications

    Moamar Sayed-Mouchaweh;Edwin Lughofer

  • Generalized smart evolving fuzzy systems

    Edwin Lughofer;Carlos Cernuda;Stefan Kindermann;Mahardhika Pratama

  • An Incremental Learning of Concept Drifts Using Evolving Type-2 Recurrent Fuzzy Neural Networks

    Mahardhika Pratama;Jie Lu;Edwin Lughofer;Guangquan Zhang

  • On-line assurance of interpretability criteria in evolving fuzzy systems – Achievements, new concepts and open issues

    Edwin Lughofer

  • Single-pass active learning with conflict and ignorance

    Edwin Lughofer

  • Learning in Non-Stationary Environments

    Moamar Sayed-Mouchaweh;Edwin Lughofer

  • pClass: An Effective Classifier for Streaming Examples

    Mahardhika Pratama;Sreenatha G. Anavatti;Meng Joo;Edwin David Lughofer

  • Autonomous data stream clustering implementing split-and-merge concepts - Towards a plug-and-play approach

    Edwin Lughofer;Moamar Sayed-Mouchaweh

  • Hybrid active learning for reducing the annotation effort of operators in classification systems

    Edwin Lughofer

  • On-line elimination of local redundancies in evolving fuzzy systems

    Edwin Lughofer;Jean-Luc Bouchot;Ammar Shaker

  • Evolving Ensemble Fuzzy Classifier

    Mahardhika Pratama;Witold Pedrycz;Edwin Lughofer

  • Residual-based fault detection using soft computing techniques for condition monitoring at rolling mills

    Francisco Serdio;Edwin Lughofer;Kurt Pichler;Thomas Buchegger

Frequent Co-Authors

Mahardhika Pratama
Mahardhika Pratama University of South Australia
Plamen Angelov
Plamen Angelov Lancaster University
Igor Škrjanc
Igor Škrjanc University of Ljubljana
José de Jesús Rubio
José de Jesús Rubio Instituto Politécnico Nacional
Jie Lu
Jie Lu University of Technology Sydney
Meng Joo Er
Meng Joo Er Dalian Maritime University
Yew-Soon Ong
Yew-Soon Ong Nanyang Technological University
Chee Peng Lim
Chee Peng Lim Swinburne University of Technology
Witold Pedrycz
Witold Pedrycz University of Alberta

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