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010   $a3-319-51370-2
024 7 $a10.1007/978-3-319-51370-6
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035   $a(DE-He213)978-3-319-51370-6
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035   $a(OCoLC)1076230917
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100   $a20170518d2017      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aUncertain Rule-Based Fuzzy Systems $eIntroduction and New Directions, 2nd Edition /$fby Jerry M. Mendel
205   $a2nd ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XXII, 684 p. 215 illus., 192 illus. in color.)
300   $aIncludes index.
311   $a3-319-51369-9 
327   $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.
330   $aThe second edition of this textbook provides a fully 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 control. In this new edition, a bottom-up approach is presented that begins by introducing classical (type-1) fuzzy sets and systems, and then explains how they can be modified to handle uncertainty. The author covers fuzzy rule-based systems ? from type-1 to interval type-2 to general type-2 ? in one volume. For hands-on experience, the book provides information on accessing MatLab and Java software to complement the content. The book features a full suite of classroom material. Presents fully updated material on new breakthroughs in human-inspired rule-based techniques for handling real-world uncertainties; Allows those already familiar with type-1 fuzzy sets and systems to rapidly come up to speed to type-2 fuzzy sets and systems; Features complete classroom material including end-of-chapter exercises, a solutions manual, and three case studies -- forecasting of time series to knowledge mining from surveys and PID control.
606   $aElectrical engineering
606   $aComputational intelligence
606   $aArtificial intelligence
606   $aNeural networks (Computer science) 
606   $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aMathematical Models of Cognitive Processes and Neural Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/M13100
615  0$aElectrical engineering.
615  0$aComputational intelligence.
615  0$aArtificial intelligence.
615  0$aNeural networks (Computer science) .
615 14$aCommunications Engineering, Networks.
615 24$aComputational Intelligence.
615 24$aArtificial Intelligence.
615 24$aMathematical Models of Cognitive Processes and Neural Networks.
676   $a511.313
700   $aMendel$b Jerry M$4aut$4http://id.loc.gov/vocabulary/relators/aut$014372
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254328903321
996   $aUncertain Rule-Based Fuzzy Systems$92283015
997   $aUNINA