His work often combines Fuzzy logic and Fuzzy control system studies. He carries out multidisciplinary research, doing studies in Fuzzy control system and Fuzzy logic. His research on Artificial intelligence often connects related topics like Fuzzy clustering. He carries out multidisciplinary research, doing studies in Fuzzy clustering and Cluster analysis. By researching both Cluster analysis and Data mining, he produces research that crosses academic boundaries. János Abonyi performs multidisciplinary study in Data mining and Artificial intelligence in his work. He carries out multidisciplinary research, doing studies in Mathematical optimization and Quadratic programming. His research combines Antecedent (behavioral psychology) and Developmental psychology. His Antecedent (behavioral psychology) study frequently draws connections between related disciplines such as Developmental psychology.
In the field of Linearization and Nonlinear model János Abonyi studies Nonlinear system. He performs multidisciplinary studies into Linearization and Nonlinear system in his work. Artificial intelligence is closely attributed to Convolution (computer science) in his research. His research ties Artificial neural network and Convolution (computer science) together. In his research, János Abonyi performs multidisciplinary study on Artificial neural network and Artificial intelligence. His multidisciplinary approach integrates Fuzzy logic and Fuzzy set in his work. Fuzzy set is closely attributed to Fuzzy rule in his research. Fuzzy rule and Fuzzy logic are two areas of study in which János Abonyi engages in interdisciplinary work. His Control (management) study often links to related topics such as Control theory (sociology).
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Cluster Analysis for Data Mining and System Identification
Janos Abonyi;Balazs Feil.
Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models
J. Abonyi;R. Babuska;F. Szeifert.
systems man and cybernetics (2002)
Supervised fuzzy clustering for the identification of fuzzy classifiers
Janos Abonyi;Ferenc Szeifert.
Pattern Recognition Letters (2003)
Learning fuzzy classification rules from labeled data
Johannes A. Roubos;Magne Setnes;Janos Abonyi.
soft computing (2003)
Fuzzy Model Identification
Fuzzy Model Identification for Control
Genetic programming for the identification of nonlinear input-output models
János Madár;János Abonyi;Ferenc Szeifert.
Industrial & Engineering Chemistry Research (2005)
Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization
Janos Abonyi;Johannes A. Roubos;Ferenc Szeifert.
International Journal of Approximate Reasoning (2003)
Modified Gath--Geva clustering for fuzzy segmentation of multivariate time-series
Janos Abonyi;Balazs Feil;Sandor Nemeth;Peter Arva.
Fuzzy Sets and Systems (2005)
Effective optimization for fuzzy model predictive control
S. Mollov;R. Babuska;J. Abonyi;H.B. Verbruggen.
IEEE Transactions on Fuzzy Systems (2004)
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