|
|
|
|
| LEADER |
03085nmm a2200337 u 4500 |
| 001 |
EB001455882 |
| 003 |
EBX01000000000000000916752 |
| 005 |
00000000000000.0 |
| 007 |
cr||||||||||||||||||||| |
| 008 |
170608 ||| eng |
| 020 |
|
|
|a 9783319513706
|
| 100 |
1 |
|
|a Mendel, Jerry M.
|
| 245 |
0 |
0 |
|a Uncertain Rule-Based Fuzzy Systems
|h Elektronische Ressource
|b Introduction and New Directions, 2nd Edition
|c by Jerry M. Mendel
|
| 250 |
|
|
|a 2nd ed. 2017
|
| 260 |
|
|
|a Cham
|b Springer International Publishing
|c 2017, 2017
|
| 300 |
|
|
|a XXII, 684 p. 215 illus., 192 illus. in color
|b online resource
|
| 505 |
0 |
|
|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
|
| 653 |
|
|
|a Computational intelligence
|
| 653 |
|
|
|a Artificial Intelligence
|
| 653 |
|
|
|a Mathematical Models of Cognitive Processes and Neural Networks
|
| 653 |
|
|
|a Computational Intelligence
|
| 653 |
|
|
|a Neural networks (Computer science)
|
| 653 |
|
|
|a Telecommunication
|
| 653 |
|
|
|a Artificial intelligence
|
| 653 |
|
|
|a Communications Engineering, Networks
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
|
| 989 |
|
|
|b Springer
|a Springer eBooks 2005-
|
| 028 |
5 |
0 |
|a 10.1007/978-3-319-51370-6
|
| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-51370-6?nosfx=y
|x Verlag
|3 Volltext
|
| 082 |
0 |
|
|a 621,382
|
| 520 |
|
|
|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
|