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Witold Pedrycz
Researcher at University of Alberta
Publications - 1966
Citations - 69104
Witold Pedrycz is an academic researcher from University of Alberta. The author has contributed to research in topics: Fuzzy logic & Fuzzy set. The author has an hindex of 101, co-authored 1766 publications receiving 58203 citations. Previous affiliations of Witold Pedrycz include University of Winnipeg & King Abdulaziz University.
Papers
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Identification of fuzzy systems by means of an auto-tuning algorithm and its application to nonlinear systems
Sung-Kwun Oh,Witold Pedrycz +1 more
TL;DR: The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of “IF …, THEN … ” statements, and exploits the theory of system optimization and fuzzy implication rules.
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Modified binary particle swarm optimization
TL;DR: A modified binary particle swarm optimization (BPSO) is presented which adopts concepts of the genotype–phenotype representation and the mutation operator of genetic algorithms, and its main feature is that the BPSO can be treated as a continuous PSO.
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An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment
TL;DR: A new distance measure for IT2FS is proposed, which is comes as a sound alternative when being compared with the existing interval type-2 fuzzy distance measures, and a decision model integrating VIKOR method and prospect theory is proposed.
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Granular clustering: a granular signature of data
Witold Pedrycz,Andrzej Bargiela +1 more
TL;DR: A new clustering algorithm is developed that organizes findings about data in the form of a collection of information granules-hyperboxes that promotes a data mining way of problem solving by emphasizing the transparency of the results.
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Allocation of information granularity in optimization and decision-making models: Towards building the foundations of Granular Computing
TL;DR: Granular models as discussed by the authors are generalizations of numeric models that are formed as a result of an optimal allocation (distribution) of information granularity, which helps establish a better rapport of the resulting granular model with the system under modeling.