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Auflistung nach Autor:in "Mendel, Jerry M."

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  • Zeitschriftenartikel
    The Perceptual Computer: the Past, Up to the Present, and Into the Future
    (Informatik Spektrum: Vol. 41, No. 1, 2018) Mendel, Jerry M.
    This article is an overview of the perceptual computer (Per-C) and summarizes developments about it for three time frames: the past (prior to 2010), up to the present (2010–2016), and into the future (2017–). It explains what the Per-C is and locates it in the Venn diagram of computing with words (CWW) in both its Intermediate CWW and Advanced CWW sectors. For the first two time frames, the article focuses on what has been done for the three component blocks of the Per-C, namely its encoder, CWW engine, and decoder. For the third time frame it focuses on what needs to be done for those three component blocks. It also gives brief summaries of published applications for the Per-C for the first two time frames. Readers who are interested in potential research topics will be most interested in the time frame (2017–).
  • Zeitschriftenartikel
    Type-2 Fuzzy Sets and Systems: a Retrospective
    (Informatik-Spektrum: Vol. 38, No. 6, 2015) Mendel, Jerry M.
    This article provides a high-level retrospective of type-2 fuzzy sets and fuzzy logic systems. It explains how type-2 fuzzy sets can be used to model membership function uncertainties, and how by doing this smoother performance can be obtained than by using type-1 fuzzy sets. It also summarizes the notation that should be used for type-2 fuzzy sets, describes four important mathematical representations for these fuzzy sets, explains the differences between type-1 and type-2 fuzzy logic systems and which of the four representations is most useful when designing an optimal type-2 fuzzy logic system, provides a very useful strategy for optimal designs of fuzzy logic systems – one that guarantees performance improvement as one goes from a type-1 fuzzy logic system to a type-2 fuzzy logic system design – , and describes four methods for simplifying the designs of type-2 fuzzy logic systems. Finally, it explains why type-2 fuzzy sets can capture two kinds of linguistic uncertainties simultaneously (the uncertainty of an individual and the uncertainties of a group about a word), whereas type-1 fuzzy sets cannot, and that such type-2 fuzzy set word models are what should be used to implement Zadeh’s Computing With Words paradigm.