04317nam 22006375 450 991025432890332120200630225739.03-319-51370-210.1007/978-3-319-51370-6(CKB)4340000000062367(DE-He213)978-3-319-51370-6(MiAaPQ)EBC6311537(MiAaPQ)EBC5596311(Au-PeEL)EBL5596311(OCoLC)1076230917(PPN)201474050(EXLCZ)99434000000006236720170518d2017 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierUncertain Rule-Based Fuzzy Systems Introduction and New Directions, 2nd Edition /by Jerry M. Mendel2nd ed. 2017.Cham :Springer International Publishing :Imprint: Springer,2017.1 online resource (XXII, 684 p. 215 illus., 192 illus. in color.)Includes index.3-319-51369-9 Introduction -- 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.The 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.Electrical engineeringComputational intelligenceArtificial intelligenceNeural networks (Computer science) Communications Engineering, Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/T24035Computational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Mathematical Models of Cognitive Processes and Neural Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/M13100Electrical engineering.Computational intelligence.Artificial intelligence.Neural networks (Computer science) .Communications Engineering, Networks.Computational Intelligence.Artificial Intelligence.Mathematical Models of Cognitive Processes and Neural Networks.511.313Mendel Jerry Mauthttp://id.loc.gov/vocabulary/relators/aut14372MiAaPQMiAaPQMiAaPQBOOK9910254328903321Uncertain Rule-Based Fuzzy Systems2283015UNINA