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02409nam a22004815i 4500 |
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978-3-319-41288-7 |
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DE-He213 |
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20160826054404.0 |
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cr nn 008mamaa |
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160826s2017 gw | s |||| 0|eng d |
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|a 9783319412887
|9 978-3-319-41288-7
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|a 10.1007/978-3-319-41288-7
|2 doi
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|d GrThAP
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|a Q342
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|a COM004000
|2 bisacsh
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|a 006.3
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|a Sanchez, Mauricio A.
|e author.
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|a Type-2 Fuzzy Granular Models
|h [electronic resource] /
|c by Mauricio A. Sanchez, Oscar Castillo, Juan R. Castro.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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|a VIII, 93 p. 60 illus., 51 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a SpringerBriefs in Applied Sciences and Technology,
|x 2191-530X
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|a Introduction -- Background and Theory -- Advances in Granular Computing -- Conclusions. .
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|a In this book, a series of granular algorithms are proposed. A nature inspired granular algorithm based on Newtonian gravitational forces is proposed. A series of methods for the formation of higher-type information granules represented by Interval Type-2 Fuzzy Sets are also shown, via multiple approaches, such as Coefficient of Variation, principle of justifiable granularity, uncertainty-based information concept, and numerical evidence based. And a fuzzy granular application comparison is given as to demonstrate the differences in how uncertainty affects the performance of fuzzy information granules.
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|a Engineering.
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|a Artificial intelligence.
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|a Computational intelligence.
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|a Engineering.
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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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| 700 |
1 |
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|a Castillo, Oscar.
|e author.
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|a Castro, Juan R.
|e author.
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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 9783319412870
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| 830 |
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|a SpringerBriefs in Applied Sciences and Technology,
|x 2191-530X
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| 856 |
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|u http://dx.doi.org/10.1007/978-3-319-41288-7
|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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