Janos Abonyi
Zitiert von
Zitiert von
Cluster analysis for data mining and system identification
J Abonyi, B Feil
Springer Science & Business Media, 2007
Instrument engineers' handbook, volume two: Process control and optimization
BG Liptak, MJ Piovoso, FG Shinskey, H Eren, GK Totherow, JE Jamison, ...
CRC press, 2018
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
J Abonyi, R Babuska, F Szeifert
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 32 …, 2002
Supervised fuzzy clustering for the identification of fuzzy classifiers
J Abonyi, F Szeifert
Pattern Recognition Letters 24 (14), 2195-2207, 2003
Fuzzy model identification
J Abonyi
Fuzzy model identification for control, 87-164, 2003
Learning fuzzy classification rules from labeled data
JA Roubos, M Setnes, J Abonyi
Information sciences 150 (1-2), 77-93, 2003
Enabling Technologies for Operator 4.0: A Survey
T Ruppert, S Jaskó, T Holczinger, J Abonyi
Applied Sciences 8 (1650), 2018
Genetic programming for the identification of nonlinear input− output models
J Madár, J Abonyi, F Szeifert
Industrial & engineering chemistry research 44 (9), 3178-3186, 2005
Modified Gath–Geva clustering for fuzzy segmentation of multivariate time-series
J Abonyi, B Feil, S Nemeth, P Arva
Fuzzy Sets and Systems 149 (1), 39-56, 2005
Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization
J Abonyi, JA Roubos, F Szeifert
International journal of approximate reasoning 32 (1), 1-21, 2003
Effective optimization for fuzzy model predictive control
S Mollov, R Babuska, J Abonyi, HB Verbruggen
IEEE Transactions on fuzzy systems 12 (5), 661-675, 2004
Fuzzy clustering and data analysis toolbox
B Balasko, J Abonyi, B Feil
Department of Process Engineering, University of Veszprem, Veszprem, 2005
Fuzzy clustering and data analysis toolbox for use with matlab
B Balasko, J Abonyi, B Feil
Veszprem, Hungary, 2005
Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard-and ontology-based methodologies and tools
S Jaskó, A Skrop, T Holczinger, T Chován, J Abonyi
Computers in industry 123, 103300, 2020
Correlation based dynamic time warping of multivariate time series
Z Bankó, J Abonyi
Expert Systems with Applications 39 (17), 12814-12823, 2012
Focal points for sustainable development strategies—Text mining-based comparative analysis of voluntary national reviews
V Sebestyén, E Domokos, J Abonyi
Journal of Environmental Management 263, 110414, 2020
Identification and control of nonlinear systems using fuzzy Hammerstein models
J Abonyi, R Babuška, MA Botto, F Szeifert, L Nagy
Industrial & engineering chemistry research 39 (11), 4302-4314, 2000
Optimization of multiple traveling salesmen problem by a novel representation based genetic algorithm
A Király, J Abonyi
Intelligent Computational Optimization in Engineering: Techniques and …, 2011
Fuzzy modeling with multivariate membership functions: Gray-box identification and control design
J Abonyi, R Babuska, F Szeifert
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 31 …, 2001
Inverse fuzzy-process-model based direct adaptive control
J Abonyi, H Andersen, L Nagy, F Szeifert
Mathematics and Computers in Simulation 51 (1-2), 119-132, 1999
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