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Igor Skrjanc - University of Ljubljana, Slovenia

Igor Škrjanc received B.S., M.S. and Ph.D. degrees in electrical engineering, in 1988, 1991 and 1996, respectively, at the Faculty of Electrical and Computer Engineering, University of Ljubljana, Slovenia. He is currently a Full Professor with the same faculty and Head of Laboratory for Autonomous and Mobile Systems. He is lecturing the basic control theory at graduate and advanced intelligent control at postgraduate study. His main research areas are adaptive, predictive, neuro-fuzzy and fuzzy adaptive control systems. His current research interests include also the field of autonomous mobile systems in sense of localization, direct visual control and trajectory tracking control. He has published 81 papers with SCI factor and 27 other journal papers. He is co-author and author of 11 chapters in international books and co-author of scientific monograph with the title Predictive approaches to control of complex systems published by Springer. He is also author and co-author of 226 conference contributions, 31 lectures at foreign universities. He is also mentor at 5 PhD thesis, 3 MSc thesis and 34 diploma works. And co-mentor of 2 PhD thesis and 1 MSc thesis. He is author of 6 university books, 24 international and domestic projects and 4 patents. In 1988 he received  the award for the best diploma work in the field of Automation, Bedjanič award, in 2007 the award of Faculty of Electrical Engineering, University of Ljubljana, Vodovnik award, for outstanding research results in the field of intelligent control, in 2012 the 1st place at the competition organized by IEEE Computational Society, Learning from the data in the frame of IEEE World Congress on Computational Intelligence 2012, Brisbane, Australia: Solving the sales prediction problem with fuzzy evolving methods, and in 2013 the best paper award at IEEE International Conference on Cybernetics in Lausanne, Switzerland. In 2008 he received the most important Slovenian research award for his work in the area of computational intelligence in control – Zois award. In year 2009 he received a Humboldt research award for long term stay and research at University of Siegen. He is also a member of IEEE CIS Standards Committee, IFAC TC 3.2 Computational Intelligence in Control Committee and Slovenian Modelling and Simulation society and Automation Society of Slovenia. He also serves as an Associated Editor for IEEE Transaction on Neural Networks and Learning System, IEEE Transaction on Fuzzy Systems, the Evolving Systems journal and International journal of artificial intelligence.

Research stay at UC3M: DEPARTMENT OF MATHEMATICS (JAN 2017 - SEP 2017)


The main goal of the research project is to develop the evolving Gustafson-Kessel possibilistic c-means clustering (eGKPCM) and evolving Gustafson-Kessel possibilistic fuzzy c-means clustering. This approach is an extension of well known possibilistic c-means clustering (PCM) which was proposed to address the drawbacks associated with the constrained membership functions used in fuzzy c-means algorithms (FCM). The possibilistic clustering employs typicality to cluster as a measure of similarity, which is no more relative membership value, but an absolute value. This has certain advantages, but also some drawbacks. The idea of possiblistic clustering is appealing when the data samples are highly noisy and the outliers are present. The extension to Gustafson-Kessel possibilistic clustering enables to deal with the clusters of different shapes and the evolving structure enables to cope with the data which vary during the time. The evolving and recursive nature of the algorithm is also suitable to solve the problems of big-data. The difference between the recursive and evolving approaches is mainly in the starting assumptions where in the recursive case we assume a constant and known number of clusters and in the case of evolving procedure the algorithm is starting with no a-priory information about the number of clusters. The algorithm in this case evolves the structure during operation by adding and removing the clusters. This approach allows the identification of very different clusters in size and shape and is also insensitive to the outliers and huge noise.