Download PDF by Francesco Masulli, Sushmita Mitra, Gabriella Pasi: Applications of Fuzzy Sets Theory

By Francesco Masulli, Sushmita Mitra, Gabriella Pasi

ISBN-10: 354073399X

ISBN-13: 9783540733997

This quantity constitutes the refereed lawsuits of the seventh foreign Workshop on Fuzzy common sense and purposes held in Camogli, Genoa, Italy in July 2007.
The eighty four revised complete papers provided including three keynote speeches have been rigorously reviewed and chosen from 147 submissions. The papers are geared up in topical sections on fuzzy set concept, fuzzy details entry and retrieval, fuzzy laptop studying, fuzzy architectures and structures; and specific classes on intuitionistic fuzzy units and tender computing in photograph processing. WILF 2007 hosts 4 unique classes, particularly the Fourth foreign assembly on Computational Intelligence tools for Bioinformatics and Biostatistics (CIDD 2007), the 3rd foreign Workshop on Cross-Language info Processing (CLIP 2007); Intuitionistic Fuzzy units: fresh Advances (IFS), and delicate Computing in snapshot Processing (CLIPS). those exact periods expand and deepen the most issues of WILF.

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Extra resources for Applications of Fuzzy Sets Theory

Example text

P(ωN )), each component of which I(ωi ) := − log p(ωi ) = log M (F )−log m(ωi ) expresses (according to the general discussion of section 1) the uncertainty measure related to the probability of occurrence of the fuzzy event ωi of the fuzzy granulation F . The relationship between these uncertainty and granularity measures of ωi is now (compare with the (16)): ∀ωi , G(ωi ) + I(ωi ) = log |X|. , it is the average fuzzy uncertainty measure related to the fuzzy granulation. 2 A Normalized Non–negative Co–entropy for Fuzzy Granulation In order to avoid the previously stressed negativity of co–entropy of a fuzzy granulation F , due to the fact that the measure distribution m(F) = m(ω1 ), m(ω2 ), .

Sometimes, for convenience, a chain will be denoted as an indexed set {xi }i∈I . 40 J. Medina, M. Ojeda-Aciego, and J. Ruiz-Calvi˜ no 1. In an orbit, we have f (xi ) = ∅ for every i ∈ I. 2. As f (xi ) is a nonempty set, there might be many possible choices for xi+1 , so we might have many possible orbits. 3. If (xi )i∈I is an orbit of f and there exists k ∈ I such that xk = xk+1 , then xk is a fixed point of f . Providing sufficient conditions for the existence of such orbits, we ensure the existence of fixed points.

Then, a possibilistic linear programming model is transformed into a crisp multi-objective programming model. Finally, Zimmermann’s fuzzy programming method [25] is applied to obtain composite single objective. Related studies on the use of fuzzy programming method to solve the fuzzy transportation problems include [8,13,14,19,21]. In this study, blending, storage and transportation costs are represented by triangular possibility distributions. The parameters of a triangular possibility distribution are given as the optimistic, the most possible, and the pessimistic values, which were estimated by a decision maker.

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Applications of Fuzzy Sets Theory by Francesco Masulli, Sushmita Mitra, Gabriella Pasi


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