Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty
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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 i...
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.
- 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.
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