W
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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Numerical and applicational aspects of fuzzy relational equations
TL;DR: Numerical methods leading to a resolution of fuzzy relational equations which create a formal description of ill-defined systems are discussed and an applicability of the numerical approach is shown in solving some problems in fuzzy systems such as identification and control.
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A generalized fuzzy Petri net model
Witold Pedrycz,Fernando Gomide +1 more
TL;DR: A new model of Petri nets based on the use of logic based neurons is proposed, aimed at neural-type modeling of the entire concept with a full exploitation of the learning capabilities of the processing units being used there.
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The design of self-organizing polynomial neural networks
TL;DR: A class of neural architectures of Polynomial Neural Networks (PNNs) are introduced, a comprehensive design methodology is discussed, a series of numeric experiments are carried out and a polynomial type of mapping between input and output variables are realized.
Book
Granular Computing and Decision-Making: Interactive and Iterative Approaches
Witold Pedrycz,Shyi-Ming Chen +1 more
TL;DR: This volume provides the reader with an updated and in-depth material on the conceptually appealing and practically sound methodology and practice of I2 Fuzzy Decision Making.
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An efficient accelerator for attribute reduction from incomplete data in rough set framework
TL;DR: A theoretic framework based on rough set theory, which is called positive approximation and can be used to accelerate a heuristic process for feature selection from incomplete data is introduced and several modified representative heuristic feature selection algorithms in roughSet theory are obtained.