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Soft Computing Applications - download pdf or read online

By Mauro L. Beretta, Andrea G. B. Tettamanzi (auth.), Prof. Andrea Bonarini, Prof. Francesco Masulli, Dr. Gabriella Pasi (eds.)

ISBN-10: 3790815446

ISBN-13: 9783790815443

ISBN-10: 3790817686

ISBN-13: 9783790817683

The papers accrued during this e-book are concerned about the applying of the so-called "soft-computing" recommendations to the purpose of defining versatile platforms. the subjects lined witness the particular learn development in the direction of an integration of precise formal thoughts for outlining versatile platforms. The contributions during this quantity in general main issue the definition of platforms in numerous software fields, akin to picture processing, regulate, asset allocation, drugs, time sequence forecasting, qualitative modeling, aid to layout, reliability research, analysis, filtering, information research, land mines detection and so on. The papers offered during this quantity are geared up into 3 major thematic sections: Fuzzy platforms, Neural Networks and Genetic and Evolutionary Algorithms, even supposing, as defined ahead of, a few works hire multiple approach from those fields.

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Extra info for Soft Computing Applications

Sample text

Various methods to compare fuzzy numbers have been proposed in the literature [5]. Fuzzy utilities, however, are not always fuzzy numbers. Besides, a comparison method makes sense only with respect to a specific application [3]. 31 In general, we have to admit the possibility that some fuzzy utilities are not comparable, so that we can establish only a partial ordering of fuzzy utilities. Therefore, we have to restate our objective by saying that we must find the set of all non-dominated investment strategies, whose fuzzy utilities are not comparable with one another, but are not "worse" than the fuzzy utility of any other investment strategy.

Now we want to find the best investment strategies in term of their fuzzy utilities. Basically, we must choose, among all wE W, a portfolio w*, such that its fuzzy utility II w . (u) is "better", or "preferable" to the fuzzy utility IIw(u) of any other w. , fuzzy sets of real numbers) with one another. Various methods to compare fuzzy numbers have been proposed in the literature [5]. Fuzzy utilities, however, are not always fuzzy numbers. Besides, a comparison method makes sense only with respect to a specific application [3].

D. Dubois and H. Prade. Theorie des possibilites. Masson, 1988. 7. D. Kelsey. The concept of uncertainty. , 17(2):61--82, 1995. 8. D. Kelsey and J. Quiggin. Theories of choice under ignorance and uncertainty. Journal ofEconomic Surveys, 6(2): 134--153, 1992. 9. A. Loraschi and A. Tettamanzi. An evolutionary algorithm for portfolio selection within a downside risk framework. In C. Dunis, editor, Forecasting Financial Markets, Series in Financial Economics and Quantitative Analysis, pages 275--285.

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Soft Computing Applications by Mauro L. Beretta, Andrea G. B. Tettamanzi (auth.), Prof. Andrea Bonarini, Prof. Francesco Masulli, Dr. Gabriella Pasi (eds.)


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