Advances in Computational Intelligence: 14th International by Andrea Sgarro, Laura Franzoi (auth.), Salvatore Greco,

By Andrea Sgarro, Laura Franzoi (auth.), Salvatore Greco, Bernadette Bouchon-Meunier, Giulianella Coletti, Mario Fedrizzi, Benedetto Matarazzo, Ronald R. Yager (eds.)

These 4 volumes (CCIS 297, 298, 299, three hundred) represent the complaints of the 14th overseas convention on info Processing and administration of Uncertainty in Knowledge-Based structures, IPMU 2012, held in Catania, Italy, in July 2012. The 258 revised complete papers awarded including six invited talks have been rigorously reviewed and chosen from a variety of submissions. The papers are prepared in topical sections on fuzzy desktop studying and online modeling; computing with phrases and determination making; delicate computing in laptop imaginative and prescient; tough units and complicated information research: concept and purposes; clever databases and knowledge approach; details fusion platforms; philosophical and methodological points of sentimental computing; uncomplicated concerns in tough units; fortieth anniversary of the measures of fuziness; SPS11 uncertainty in profiling platforms and purposes; dealing with uncertainty with copulas; formal the right way to care for uncertainty of many-valued occasions; linguistic summarization and outline of information; fuzzy implications: conception and purposes; sensing and knowledge mining for instructing and studying; thought and functions of intuitionistic fuzzy units; approximate points of knowledge mining and database analytics; fuzzy numbers and their purposes; info processing and administration of uncertainty in knowledge-based structures; aggregation services; obscure possibilities; probabilistic graphical versions with imprecision: thought and purposes; trust functionality idea: fundamentals and/or purposes; fuzzy uncertainty in economics and enterprise; new developments in De Finetti's strategy; fuzzy measures and integrals; multicriteria choice making; uncertainty in privateness and safety; uncertainty within the spirit of Pietro Benvenuti; coopetition; video game thought; probabilistic approach.

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Extra resources for Advances in Computational Intelligence: 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Catania, Italy, July 9-13, 2012, Proceedings, Part III

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A ranking method is proposed for interval-valued fuzzy numbers based on their mean value and variance and we showed an application to capital budgeting decisions when the revenues from projects are represented by interval-valued trapezoidal fuzzy numbers. References 1. : Time to market prediction using type-2 fuzzy sets. J. Manuf. Tech. Manage. 17, 513–520 (2006) 2. : Capital budgeting problems with fuzzy cash flows. Mathware Soft Comput. 6, 81–89 (1999) 28 C. Carlsson, R. Full´er, and J. Mezei 3.

Then we can rank projects according to the mean value of their fuzzy net present value and if we find two projects with maximal value, we choose the one with smaller variance. 6 Conclusions We have introduced the interval-valued expectation for interval-valued fuzzy numbers. The crisp mean value is the arithmetic mean of the lower and upper mean values of the IVFN. We have shown that this mean operator is linear with respect to the extended addition and multiplication by a scalar of interval-valued fuzzy numbers.

An IVFN is also a special case of type-2 fuzzy sets introduced by Zadeh [23]. For the γ-level sets of A∗ and A∗ we will use the notations [A∗ ]γ = [a1 (γ), a2 (γ)], ∗ γ [A ] = [A1 (γ), A2 (γ)] and [A]γ = ([A∗ ]γ , [A∗ ]γ ). If A, B ∈ IVFN, then their sum A + B is defined by the max-min extension principle, with lower fuzzy set A∗ + B∗ and upper fuzzy set A∗ + B ∗ , that is, [A + B]γ = ([A∗ + B∗ ]γ , [A∗ + B ∗ ]γ ) = [A]γ + [B]γ for all γ ∈ [0, 1]. If A ∈ IVFN, then B ∈ F is an embedded fuzzy number of A if A∗ (u) ≤ B(u) ≤ A∗ (u), for all u ∈ R.

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