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Type-2 Fuzzy Granular Models / by Mauricio A. Sanchez, Oscar Castillo, Juan R. Castro.
Tipo de material:
TextoSeries SpringerBriefs in Computational Intelligence | | SpringerBriefs in Computational Intelligence | Cham :Springer International Publishing :Imprint: Springer,2017Descripción: VIII, 93 p. 60 illus., 51 illus. in color; online resourceTipo de contenido: - texto
- computadora
- recurso en línea
- 9783319412887
- 006.3
- 23
Contenidos:
En: Springer eBooksResumen: 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.
Introduction -- Background and Theory -- Advances in Granular Computing -- Conclusions. .
| Tipo de ítem | Biblioteca actual | Colección | Signatura topográfica | Estado | Fecha de vencimiento | Código de barras | |
|---|---|---|---|---|---|---|---|
| Libro Electrónico (LE) | Biblioteca Virtual | Colección Electrónica (CE) | Disponible | BIV0007602 |
Introduction -- Background and Theory -- Advances in Granular Computing -- Conclusions. .
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.