Sanchez, Mauricio A.
Type-2 Fuzzy Granular Models [electronic resource] / by Mauricio A. Sanchez, Oscar Castillo, Juan R. Castro. - 1st ed. 2017. - VIII, 93 p. 60 illus., 51 illus. in color. online resource. - SpringerBriefs in Computational Intelligence, 2625-3712 . - SpringerBriefs in Computational Intelligence, .
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.
9783319412887
10.1007/978-3-319-41288-7 doi
Computational intelligence.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence.
Q342
006.3
Type-2 Fuzzy Granular Models [electronic resource] / by Mauricio A. Sanchez, Oscar Castillo, Juan R. Castro. - 1st ed. 2017. - VIII, 93 p. 60 illus., 51 illus. in color. online resource. - SpringerBriefs in Computational Intelligence, 2625-3712 . - SpringerBriefs in Computational Intelligence, .
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.
9783319412887
10.1007/978-3-319-41288-7 doi
Computational intelligence.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence.
Q342
006.3