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Edwin Lughofer

Edwin Lughofer's picture FLLL 043 (0) 7236 3343 - 431
  • imPACts (K-Project): Industrial Methods for Process Analytical Chemistry – From Measurement Technologies to Information Systems (Key Researcher of MP3)
  • mvControl (FFG "IKT of the Future"): Generating process feedback from heterogeneous data sources in quality control; in collaboration with the coordinator Profactor and Sony DADC Austria (Key Researcher)
  • useML (FFG "IKT of the Future"): Improving the usability of machine learning in industrial inspection systems; in collaboration with the coordinator Profactor and 2 industrial partners (Key Researcher)
  • Increasing the Transparency of LCM/ACCM in International Research Fora: Organization and Publication Activities on International Level with the support of Linz Center of Mechatronics / Austrian Center of Competence in Mechatronics
  • HOPL (K-Project): Heuristic Optimization in Production and Logistics
  • TransLearn (SRP) - Transfer Learning with Soft Computing Models for Regression Problems: Strategic Research Project with SCCH
  • AEDA (K-Project): Advanced Engineering Design Automation
  • PAC (K-Project): Process Analytical Chemistry - Data Acquistion and Data Processing (Key Researcher in SP1); National K-Project sponsored by the FFG, 9 industrial and 7 academic research partners
  • IREFS (bilateral FWF/DFG research project): Interpretable and Reliable Evolving Fuzzy Systems (Initiator)
  • Condition Monitoring with Data-Driven Models: Strategic Project with ACCM (Area 6) (Key Researcher)
  • Performance Optimization of Electrical Drives: Strategic Project with ACCM (Area 4) (Key Researcher)
  • ASHMOSD (National Research Project):  Austrian Structural Health Monitoring System Demonstrator
  • DynaVis (EU-Project): Dynamically adaptive image classification framework; combining machine learning with image processing techniques:; technical representative of JKU (Key Researcher)
  • SynteX (EU-Project): Measuring Feelings and Expectations Associated with Textures:
  • Technology Transfer sponsored by the Upperaustrian technology and research promotion
  • AMPA (EU-Project): Automatic Measurement Plausibility Analysis at engine test benches: research and development in data-based modelling, nonlinear system identification and fault detection; technical representative of JKU (Key Researcher) in AMPA EU-Project; together with 8 partners in Europe
  • Exchange of know-how in data-driven evolving fuzzy systems with Lancaster University, sponsored by the Royal Society Grant, United Kingdom

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):


Book Chapters:

  • 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

Position Papers and Editorials:

Journal Papers:

  • Edwin Lughofer*, Mahardhika Pratama, On-line Active Learning in Data Stream Regression using Uncertainty Sampling based on Evolving Generalized Fuzzy Models, IEEE Transactions on Fuzzy Systems, on-line and in press, 2017, DOI: 10.1109/TFUZZ.2017.2654504

  • Francisco Serdio, Edwin Lughofer*, Ciprian Zavoianu, Kurt Pichler, Markus Pichler, Thomas Buchegger, Hajrudin Efendic, Improved Fault Detection employing Hybrid Memetic Fuzzy Modeling and Adaptive Filters, Applied Soft Computing, vol. 51, pp. 60-82, 2017,

  • Choiru Zain*, Mahardhika Pratama, Edwin Lughofer, Sreenatha Anavatti, Evolving Type-2 Web News Mining, Applied Soft Computing, vol. 54, pp. 200-220, 2017,

  • Edwin Lughofer*, Stefan Kindermann, Mahardhika Pratama and Jose de Jesus Rubio. Top-Down Sparse Fuzzy Regression Modeling from Data with Improved Coverage, International Journal of Fuzzy Systems, on-line and in press, 2016, doi:10.1007/s40815-016-0271-0

  • Mahardhika Pratama*, Edwin Lughofer, Meng Joo Er and Chee-Peng Lim, Data Driven Modeling based on Recurrent Interval-Valued Metacognitive Scaffolding Fuzzy Neural Network, Neurocomputing, to appear, 2016

  • José de Jesús Rubio*, L. Zhang, E. Lughofer, P. Cruz, A. Alsaedi, T. Hayat. Modeling and control with neural networks for a magnetic levitation system. Neurocomputing, vol. 227, pp. 113-121, 2016,

  • Mahardhika Pratama*, Jie Lu, Edwin Lughofer, Guang Zhang and Meng Joo Er, Incremental Learning of Concept Drift Using Evolving Type-2 Recurrent Fuzzy Neural Network, IEEE Transactions on Fuzzy Systems, on-line and in press, 2016, 10.1109/TFUZZ.2016.2599855

  • Mahardhika Pratama, Edwin Lughofer, Chee Peng Lim, Wenny Rahayu, Taram Dillon and Agus Budiyono, pClass+: A novel Evolving Semi-supervised Classifier, International Journal of Fuzzy Systems, on-line and in press, 2016, DOI: 10.1007/s40815-016-0236-3

  • 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

  • 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

  • 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

  • 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 32, "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

  • 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,

  • 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
  • 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
  • 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

* corresponding author(s)
Full List of Conference Papers:  Full List
Recent Conference Papers:

  • E. Lughofer, R. Pollak, A.C. Zavoianu, P. Meyer-Heye, H. Zörrer, C. Eitzinger, J. Haim, T. Radauer, Self-Adaptive Time-Series Based Forecast Models for Predicting Quality Criteria in Microfluidics Chip Production, Proceedings of the IEEE Cybernetics Conference 2017, Exeter, U.K., 2017, to appear.
  • R. Nikzad Langerodi, E. Lughofer, T. Reischer, W. Kantner, M. Pawliczek and M. Brandstetter, Improved drift handling in melamine resin production by ensemble partial least squares and incremental Page-Hinkley testing, Proceedings of the EuroAnalysis 2017 Conference, Stockholm, Sweden, to appear.
  • E. Lughofer, M. Pratama and I. Skrjanc, Incremental Rule Splitting in Generalized Evolving Fuzzy Regression Models, Proceedings of the IEEE Evolving and Adaptive Intelligent Systems Conference (IEEE EAIS 2017), Ljubljana, 2017.
  • W. Zellinger, E. Lughofer, S. Saminger-Platz, T. Grubinger and T. Natschläger.
    Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning. Proceedings of the 5th International Conference on Learning Representations (ICLR 2017), to appear, Toulon, France, 2017.
  • M. Pratama, P.P. Angelov, J. Lu, E. Lughofer, M. Seera and C.P. Lim. A Randomized Neural Network for Data Streams. Proceedings of the International Joint Conference on Neural Networks (IJCNN 2017), to appear, Anchorage, Alaska, U.S.A.
  • G. Andonovski, E. Lughofer and I. Skrjanc. A Comparison of RECCO and FCPFC Controller on Nonlinear Chemical Reactor. The 36th IASTED International Conference on Modelling, Identification and Control, Innsbruck, Austria, 2017, to appear.