D-Index & Metrics Best Publications

János Abonyi

University of Pannonia
Hungary

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Engineering and Technology D-index 31 Citations 5,335 263 World Ranking 7268 National Ranking 14

Overview

What is he best known for?

The fields of study János Abonyi is best known for:

Mathematical optimization
Machine learning
Fractionating column

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.

His most cited work include:

Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models (267 citations)
Effective Optimization for Fuzzy Model Predictive Control (124 citations)

What are the main themes of his work throughout his whole career to date

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).

János Abonyi most often published in these fields:

Artificial intelligence (81.82%)
Fuzzy logic (72.73%)
Control (management) (54.55%)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Cluster Analysis for Data Mining and System Identification

Janos Abonyi;Balazs Feil.
(2007)

486 Citations

Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models

J. Abonyi;R. Babuska;F. Szeifert.
systems man and cybernetics (2002)

385 Citations

Supervised fuzzy clustering for the identification of fuzzy classifiers

Janos Abonyi;Ferenc Szeifert.
Pattern Recognition Letters (2003)

328 Citations

Learning fuzzy classification rules from labeled data

Johannes A. Roubos;Magne Setnes;Janos Abonyi.
soft computing (2003)

295 Citations

Fuzzy Model Identification

János Abonyi.
(2003)

295 Citations

Fuzzy Model Identification for Control

Janos Abonyi.
(2003)

211 Citations

Genetic programming for the identification of nonlinear input-output models

János Madár;János Abonyi;Ferenc Szeifert.
Industrial & Engineering Chemistry Research (2005)

199 Citations

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)

187 Citations

Modified Gath--Geva clustering for fuzzy segmentation of multivariate time-series

Janos Abonyi;Balazs Feil;Sandor Nemeth;Peter Arva.
Fuzzy Sets and Systems (2005)

178 Citations

Effective optimization for fuzzy model predictive control

S. Mollov;R. Babuska;J. Abonyi;H.B. Verbruggen.
IEEE Transactions on Fuzzy Systems (2004)

169 Citations

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Frequent Co-Authors

External Links

Personal Website for János Abonyi