03425nam a22004575i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000900153072001600162072002300178072001500201100007800216245009700294250001800391264007500409300006900484336002600553337002600579338003600605347002400641505056100665520123001226650003202456650002302488650002902511650004202540650003202582650004202614650002902656650006802685710003402753773002602787776003602813776003602849776003602885856004602921978-3-031-35378-9DE-He21320251030111411.0cr nn 008mamaa240201s2024    sz |    s    |||| 0|eng d  a97830313537897 a10.1007/978-3-031-35378-92doi 4aQ342 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema1 aMendel, Jerry M.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aExplainable Uncertain Rule-Based Fuzzy Systemsh[electronic resource] /cby Jerry M. Mendel.  a3rd ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXXIII, 580 p. 257 illus., 231 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aIntroduction -- Part 1: Type-1 Fuzzy Sets and Systems -- Short Primers on Type-1 Fuzzy Sets and Fuzzy Logic -- Type-1 Fuzzy Logic Systems -- Part 2: Type-2 Fuzzy Sets -- Sources of Uncertainty -- Type-2 Fuzzy Sets -- Operations on and Properties OF Type-2 Fuzzy Sets -- Type-2 Relations and Compositions -- Centroid of a Type-2 Fuzzy Set: Type-Reduction -- Part 3: Type-2 Fuzzy Logic Systems -- Mamdani Interval Type-2 Fuzzy Logic Systems (IT2 FLSS) -- TSK Interval Type-2 Fuzzy Logic Systems -- General Type-2 Fuzzy Logic Systems (GT2 FLSS) -- Conclusion.  aThe third edition of this textbook presents a further updated approach to fuzzy sets and systems that can model uncertainty — i.e., “type-2” fuzzy sets and systems. The author demonstrates how to overcome the limitations of classical fuzzy sets and systems, enabling a wide range of applications, from time-series forecasting to knowledge mining to classification to control and to explainable AI (XAI). This latest edition again begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty, leading to type-2 fuzzy sets and systems. New material is included about how to obtain fuzzy set word models that are needed for XAI, similarity of fuzzy sets, a quantitative methodology that lets one explain in a simple way why the different kinds of fuzzy systems have the potential for performance improvements over each other, and new parameterizations of membership functions that have the potential for achieving even greater performance for all kinds of fuzzy systems. For hands-on experience, the book provides information on accessing MATLAB, Java, and Python software to complement the content. The book features a full suite of classroom material. 0aComputational intelligence. 0aTelecommunication. 0aArtificial intelligence. 0aNeural networks (Computer science) .14aComputational Intelligence.24aCommunications Engineering, Networks.24aArtificial Intelligence.24aMathematical Models of Cognitive Processes and Neural Networks.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303135377208iPrinted edition:z978303135379608iPrinted edition:z978303135380240uhttps://doi.org/10.1007/978-3-031-35378-9