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Edwin Lughofer
Edwin Lughofer's picture
edwin.lughofer@jku.at FLLL 043 (0) 7236 3343 - 431

PROJECTS:

 
ACTIVITIES (Organizing, Editing):

  • Co-Organizer of 15 Special Sessions in the fields of Evolving Systems and Machine Learning at International Conferences since 2010

 
ACTIVITIES (Keynotes, Committee Memberships, Reviewing):

PUBLICATIONS:
BOOKS:

 
BOOK CHAPTERS:
 

  • E. Lughofer and M. Sayed-Mouchaweh. Prologue --- Predictive Maintenance in Dynamic Systems. Predictive Maintenance in Dynamic Systems, editors: E. Lughofer and M. Sayed-Mouchaweh, Springer, pp. 1-23, 2019.
     
  • E. Lughofer, A.C. Zavoianu, M. Pratama and T. Radauer. Automated Process Optimization in Manufacturing Systems based on Static and Dynamic Prediction Models. in: Predictive Maintenance in Dynamic Systems, editors: E. Lughofer and M. Sayed-Mouchaweh, Springer, 486-531, 2019.
     
  • M. Pratama, A. Ashfahani, E. Lughofer and S. Huang. An Evolving RFID Localization Model in The Manufacturing Shopfloor. in: Predictive Maintenance in Dynamic Systems, editors: E. Lughofer and M. Sayed-Mouchaweh, Springer, pp. 287-309, 2019.
     
  • Edwin Lughofer, Robust Data-Driven Fault Detection in Dynamic Process Environments Using Discrete Event Systems. in: Diagnosability, Security and Safety of Hybrid Dynamic and Cyber-Physical Systems, editor: M. Sayed-Mouchaweh, pp. 73--116, 2018.
     
  • Edwin Lughofer, Evolving Fuzzy Systems --- Fundamentals, Reliability, Interpretability, Useability, Applications (a comprehensive work of reference), in: Handbook on Computational Intelligence, editor: Plamen Parvanov Angelov, World Scientific, pp. 67-135, 2016   -  DOWNLOAD
     
  • Edwin Lughofer, Flexible Evolving Fuzzy Inference Systems from Data Streams (FLEXFIS++), in: Learning in Non-Stationary Environments: Methods and Applications, editors: Moamar Sayed-Mouchaweh and Edwin Lughofer, Springer, New York, 2012, pp. 205-246
     
  • Edwin Lughofer, Christian Eitzinger and Carlos Guardiola, On-line Quality Control with Flexible Evolving Fuzzy Systems, in: Learning in Non-Stationary Environments: Methods and Applications, editors: Moamar Sayed-Mouchaweh and Edwin Lughofer, Springer, New York, 2012, pp. 375-406
     
  • Davy Sannen, Jean-Michel Papy, Steve Vandenplas, Edwin Lughofer and Hendrik van Brussel, Incremental Classifier Fusion and its Application in Industrial Monitoring and Diagnostics, in: Learning in Non-Stationary Environments: Methods and Applications, editors: Moamar Sayed-Mouchaweh and Edwin Lughofer, Springer, New York, 2012, pp. 153-184
     
  • Edwin Lughofer, Evolving Fuzzy Models - Incremental Learning, Stability and Interpretability Issues, Applications, VDM Verlag, Saarbrücken, 2008 (book issue of PhD thesis)
     
  • Edwin Lughofer, Data-Driven Incremental Learning of Takagi-Sugeno Fuzzy Models, PhD-Thesis, Department of Knowledge-Based Mathematical Systems, University Linz, 2001-2005
     
  • Edwin Lughofer. Towards Robust Evolving Fuzzy Systems, book chapter in Evolving Intelligent Systems - Methodologies and Applications, editors: Plamen Angelov, Dimitar Filev and Nik Kasabov, John Wiley and Sons, 2010, pp. 87-126
     
  • Erich Peter Klement*, Edwin Lughofer, Johannes Himmelbauer and Bernhard Moser, Data-Driven and Knowledge-Based Modelling, chapter in Hagenberg Research, editors: Michael Affenzeller, Bruno Buchberger, Alois Ferscha, Michael Haller, Tudor Jebelean, Erich Peter Klement, Josef Kueng, Peter Paule, Birgit Proell, Wolfgang Schreiner, Gerhard Weiss, Roland Wagner, Wolfram Woess, Robert Stubenrauch and Wolfgang Windsteiger, Springer Verlag, pp. 237-279, 2009
     
  • Christian Eitzinger*, James E. Smith, Edwin Lughofer and Davy Sannen, Lernfaehige Inspektionssysteme, Automatisierungsatlas, SPS Magazin, 2009, pp. 370-372

 
JOURNAL PAPERS:

 

  • Carlos Cernuda, Edwin Lughofer*, Helmut Klein, Clemens Forster, Marcin Pawliczek and Markus Brandstetter, Improved Quantification of Important Beer Quality Parameters based on Non-linear Calibration Methods applied to FT-MIR Spectra, Analytical and Bioanalytical Chemistry (special issue on "Process Analytics" organized by Rudolf Kessler), vol. 409 (3), pp. 841-857, 2016, 10.1007/s00216-016-9785-4
     

  • Gerd Bramerdorfer*, Alexandru-Ciprian Zavoianu, Siegfried Silber, Edwin Lughofer, Wolfgang Amrhein, Possibilities for Speeding-Up the FE-Based Optimization of Electrical Machines - A Case Study, IEEE Transactions on Industrial Applications, vol. 52 (6), pp. 4668-4677, 2016, 10.1109/TIA.2016.2587702
     

  • Edwin Lughofer*, Eva Weigl, Wolfgang Heidl, Christian Eitzinger and Thomas Radauer, Recognizing Input Space and Target Concept Drifts in Data Streams with Scarcely Labelled and Unlabelled Instances, Information Sciences, vol. 355-356, pp. 127-151, 2016, doi:10.1016/j.ins.2016.03.034 (cited 37 times, Google Scholar)
     

  • Mahardhika Pratama* and Jie Lu and E. Lughofer and G. Zhang and Sreenatha Anavatti, Scaffolding Type-2 Classifier for Incremental Learning under Concept Drifts, NeuroComputing, vol. 191, pp. 304-329, 2016, doi:10.1016/j.neucom.2016.01.049 (cited 81 times, Google Scholar, "h")
     

  • Eva Weigl*, Wolfgang Heidl, Edwin Lughofer, Christian Eitzinger and Thomas Radauer, On Improving Performance of Surface Inspection Systems by On-line Active Learning and Flexible Classifier Updates, Machine Vision and Applications, vol. 27 (1), pp. 103-127, 2016, doi: 10.1007/s00138-015-0731-9
     

  • Edwin Lughofer*, Carlos Cernuda, Stefan Kindermann and Mahardhika Pratama, Generalized Smart Evolving Fuzzy Systems, Evolving Systems, vol. 6 (4), pp. 269-292, 2015, doi: 10.1007/s12530-015-9132-6 (cited 137, Google Scholar, "h").
     

  • Edwin Lughofer* and Moamar Sayed-Mouchaweh, Autonomous Data Stream Clustering Implementing Split-and-Merge Techniques - Towards a Plug-and-Play Approach, Information Sciences, vol. 204, pp. 54--79, 2015 (cited 99, "h", Google Scholar).
     

  • Edwin Lughofer*, Eva Weigl, Wolfgang Heidl, Christian Eitzinger, Thomas Radauer, Integrating new Classes On the Fly in Evolving Fuzzy Classifier Designs and Its Application in Visual Inspection, Applied Soft Computing, vol. 35, pp. 558-582, 2015, doi:10.1016/j.asoc.2015.06.038  (cited 37 times, Google Scholar)
     

  • Jianli Liu*, Edwin Lughofer and Xianyi Zeng, Aesthetic Perception of Visual Textures: A Holistic Exploration using Texture Analysis, Psychological Experiment and Perception Modeling, Frontiers of Computational Neuroscience, vol. 9:134, pp. 1--14, 2015, http://dx.doi.org/10.3389/fncom.2015.00134
     

  • Carlos Cernuda, Edwin Lughofer*, Thomas Röder, Wolfgang Märzinger, Thomas Reischer, Marcin Pawliczek and Markus Brandstätter, Self-Adaptive Non-Linear Methods for Improved Multivariate Calibration in Chemical Processes, Lenzinger Berichte, vol. 92, pp. 12--32, 2015
     
  • Kurt Pichler*, Edwin Lughofer, Markus Pichler, Thomas Buchegger, Erich Peter Klement and Matthias Huschenbett, Fault detection in reciprocating compressor valves under varying load conditions, Mechanical Systems and Signal Processing, vol. 70-71, pp. 104-119, 2016, doi:10.1016/j.ymssp.2015.09.005
    (cited 66, Google Scholar, "h").
     
  • Mahardhika Pratama*, Sreenatha Anavatti, Edwin Lughofer, C.P. Lim, An Incremental Meta-cognitive-based Scaffolding Fuzzy Neural Network, NeuroComputing, vol. 171, pp. 89-105, 2016, doi:10.1016/j.neucom.2015.06.022 (cited 90, Google Scholar, "h").
     
  • Alexandru-Ciprian Zavoianu*, Edwin Lughofer, Werner Koppelstaetter, Günther Weidenholzer, Wolfgang Amrhein, Erich Peter Klement, Performance Comparison of Generational and Steady-State Asynchronous Multi-Objective Evolutionary Algorithms for Computationally-Intensive Problems, Knowledge-Based Systems, vol. 87, pp. 47-60, 2015, doi:10.1016/j.knosys.2015.05.029
     
  • Jianli Liu*, Edwin Lughofer, Xianyi Zeng, Could Linear Model Bridge the Gap between Low-level Statistical Features and Aesthetic Emotions of Visual Textures?, NeuroComputing, vol. 168 (30), pp. 947-960, 2015, doi:10.1016/j.neucom.2015.05.030
     
  • Francisco Serdio, Edwin Lughofer*, Kurt Pichler, Markus Pichler, Thomas Buchegger and Hajrudin Efendic, Fuzzy Fault Isolation using Gradient Information and Quality Criteria from System Identification Models, Information Sciences, vol. 316, pp. 18-39, 2015, doi:10.1016/j.ins.2015.04.008