Advanced Concepts in Fuzzy Logic and Systems with Membership by Janusz T. Starczewski

By Janusz T. Starczewski

This e-book generalizes fuzzy common sense structures for various different types of uncertainty, including

- semantic ambiguity due to constrained belief or lack of knowledge approximately specific club functions

- loss of attributes or granularity bobbing up from discretization of genuine data

- vague description of club functions

- vagueness perceived as fuzzification of conditional attributes.

Consequently, the club uncertainty will be modeled by way of combining equipment of traditional and type-2 fuzzy common sense, tough set idea and threat theory.

In specific, this publication presents a few formulae for enforcing the operation prolonged on fuzzy-valued fuzzy units and provides a few uncomplicated constructions of generalized doubtful fuzzy common sense structures, in addition to introduces numerous of ways to generate fuzzy club uncertainty. it truly is fascinating as a reference publication for under-graduates in better schooling, grasp and health practitioner graduates within the classes of desktop technology, computational intelligence, or fuzzy regulate and class, and is mainly devoted to researchers and practitioners in undefined.

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Additional info for Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty

Sample text

We may compare rough approximations of a fuzzy set A to the possibility and necessity measures of a classical set X. The lower approximation is the membership degree of object x which certainly belongs to fuzzy set A, and the upper approximation is the membership grade of x which possibly belongs to A, whereas the possibility and necessity express boundary degrees of certainty that either possibly or necessarily an object x of possibilistic distribution, let us say μA , is labeled with X. Therefore, these concepts are opposed to each other.

Intuitionistic fuzzy sets: theory and applications. Studies in Fuzziness and Soft Computing, vol. 35. : Fuzzy implications. Studies in Fuzziness and Soft Computing, vol. 231. Springer, Heidelberg (2008) Borkowski, L. ): Selected Works of J. : Vague sets are intuitionistic fuzzy sets. : Fuzzy sets and systems: Theory and applications. : Necessity measures and the resolution principle. : Possibility Theory. : Resolution principles in possibilistic logic. : Rough fuzzy sets and fuzzy rough sets.

R(A) (x) = inf {μA (x) |x ∈ Xi } , μR(A) (x) = sup {μA (x) |x ∈ Xi } . 5 (x − 5) 22 , for the same partition as in Fig. 4; note that in subfigure (d), the rough-fuzzy set is interpreted as refined piecewise constant functions on the whole universe X. We may compare rough approximations of a fuzzy set A to the possibility and necessity measures of a classical set X. The lower approximation is the membership degree of object x which certainly belongs to fuzzy set A, and the upper approximation is the membership grade of x which possibly belongs to A, whereas the possibility and necessity express boundary degrees of certainty that either possibly or necessarily an object x of possibilistic distribution, let us say μA , is labeled with X.

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