Analyzes the general properties of some core concepts of Granular Computing, such as the information granulation, the principle of justifiable granularity, and higher-type information granule formation
All information granules are represented via Fuzzy Sets and all proposed approaches derivate of hybrid intelligent algorithms, such that they automate the modeling from raw data to final fuzzy granular model
Several contributions to the area of Granular Computing are presented: a nature inspired granulating algorithm, various techniques for forming higher-type information granules, and an application comparison of various types of information granules
Reference for engineers who wish to dwell into applications of more complex algorithms inspired by Granular Computing, for aspiring graduate students who desire to better understand how information granules can be formed, and for scientists who want to keep up to date on current research trends on Granular Computing
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.
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.
Inhaltsverzeichnis
Introduction.- Background and Theory.- Advances in Granular Computing.- Conclusions.
Klappentext
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.
Analyzes the general properties of some core concepts of Granular Computing, such as the information granulation, the principle of justifiable granularity, and higher-type information granule formation
rn
All information granules are represented via Fuzzy Sets and all proposed approaches derivate of hybrid intelligent algorithms, such that they automate the modeling from raw data to final fuzzy granular model
rn
Several contributions to the area of Granular Computing are presented: a nature inspired granulating algorithm, various techniques for forming higher-type information granules, and an application comparison of various types of information granules
rn
Reference for engineers who wish to dwell into applications of more complex algorithms inspired by Granular Computing, for aspiring graduate students who desire to better understand how information granules can be formed, and for scientists who want to keep up to date on current research trends on Granular Computing
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