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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.
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Hybrid intelligent systems : analysis and design
TL;DR: In this article, a comparative study of Controllers using Type-2 and Type-1 Fuzzy Logic Control is presented, comparing the stability and robustness of two types of controllers.
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Logic-based fuzzy networks: A study in system modeling with triangular norms and uninorms
Xiaofeng Liang,Witold Pedrycz +1 more
TL;DR: This study is concerned with the design and the development of transparent logic networks realized with the aid of fuzzy neurons and fuzzy unineurons and introduces a new discretization environment that is realized by means of particle swarm optimization (PSO) and data clustering implemented by the K-Means algorithm.
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Learning fuzzy cognitive maps with required precision using genetic algorithm approach
TL;DR: Comprehensive experiments reveal that the complete design of FCMs through learning carried out on experimental data helps design models of required accuracy in an automated manner.
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A programmable triangular neighborhood function for a Kohonen self-organizing map implemented on chip
TL;DR: A new hardware implementation of the triangular neighborhood function (TF) for ultra-low power, self-organizing maps (SOM) is presented and it is shown that even for low signal resolutions (3–6 bits) performance of the network is not affected.
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A Piecewise Aggregate pattern representation approach for anomaly detection in time series
Huorong Ren,Huorong Ren,Mingming Liu,Mingming Liu,Zhiwu Li,Zhiwu Li,Witold Pedrycz,Witold Pedrycz,Witold Pedrycz +8 more
TL;DR: This work incorporates the PAPR method into anomaly detection by computing the similarity of patterns and using a Random Walk model, as a classifier, to determine the similarity values.