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03604nam a22004935i 4500 |
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978-3-319-51370-6 |
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DE-He213 |
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20170518085020.0 |
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170518s2017 gw | s |||| 0|eng d |
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|a 9783319513706
|9 978-3-319-51370-6
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|a 10.1007/978-3-319-51370-6
|2 doi
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|a 621.382
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|a Mendel, Jerry M.
|e author.
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|a Uncertain Rule-Based Fuzzy Systems
|h [electronic resource] :
|b Introduction and New Directions, 2nd Edition /
|c by Jerry M. Mendel.
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|a 2nd ed. 2017.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XXII, 684 p. 215 illus., 192 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
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|a online resource
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|a text file
|b PDF
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|a 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.
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|a 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.
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|a Engineering.
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|a Artificial intelligence.
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|a Neural networks (Computer science).
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|a Computational intelligence.
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|a Electrical engineering.
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|a Engineering.
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|a Communications Engineering, Networks.
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|a Computational Intelligence.
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| 650 |
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|a Artificial Intelligence (incl. Robotics).
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| 650 |
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|a Mathematical Models of Cognitive Processes and Neural Networks.
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| 710 |
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|a SpringerLink (Online service)
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|t Springer eBooks
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| 776 |
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|i Printed edition:
|z 9783319513690
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|u http://dx.doi.org/10.1007/978-3-319-51370-6
|z Full Text via HEAL-Link
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
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