Advanced Concepts In Fuzzy Logic And Systems With Membership Uncertainty (studies In Fuzziness And Soft Computing)
by Janusz T. Starczewski /
2012 / English / PDF
7.5 MB Download
This book generalizes fuzzy logic systems for different types of
uncertainty, including - semantic ambiguity resulting from limited
perception or lack of knowledge about exact membership functions -
lack of attributes or granularity arising from discretization of
real data - imprecise description of membership functions -
vagueness perceived as fuzzification of conditional attributes.
Consequently, the membership uncertainty can be modeled by
combining methods of conventional and type-2 fuzzy logic, rough set
theory and possibility theory.
In
particular, this book provides a number of formulae for
implementing the operation extended on fuzzy-valued fuzzy sets and
presents some basic structures of generalized uncertain fuzzy logic
systems, as well as introduces several of methods to generate fuzzy
membership uncertainty. It is desirable as a reference book for
under-graduates in higher education, master and doctor graduates in
the courses of computer science, computational intelligence, or
fuzzy control and classification, and is especially dedicated to
researchers and practitioners in industry.
This book generalizes fuzzy logic systems for different types of
uncertainty, including - semantic ambiguity resulting from limited
perception or lack of knowledge about exact membership functions -
lack of attributes or granularity arising from discretization of
real data - imprecise description of membership functions -
vagueness perceived as fuzzification of conditional attributes.
Consequently, the membership uncertainty can be modeled by
combining methods of conventional and type-2 fuzzy logic, rough set
theory and possibility theory.
In
particular, this book provides a number of formulae for
implementing the operation extended on fuzzy-valued fuzzy sets and
presents some basic structures of generalized uncertain fuzzy logic
systems, as well as introduces several of methods to generate fuzzy
membership uncertainty. It is desirable as a reference book for
under-graduates in higher education, master and doctor graduates in
the courses of computer science, computational intelligence, or
fuzzy control and classification, and is especially dedicated to
researchers and practitioners in industry.