Statistical Modeling, Analysis and Management of Fuzzy Data (Studies in Fuzziness and Soft Computing) - kelloggchurch.org

Statistical Modeling, Analysis and Management of Fuzzy Data.

: Statistical Modeling, Analysis and Management of Fuzzy Data Studies in Fuzziness and Soft Computing 9783790825015: Bertoluzza, Carlo: Books. : Statistical Modeling, Analysis and Management of Fuzzy Data Studies in Fuzziness and Soft Computing 9783790814408: Bertoluzza, Carlo, Gil, Maria A. The contributions in this book connect Probability Theory/Statistics and Fuzzy Set Theory in different ways. Some of these connections are either philosophical or theoretical in nature, but most of th. "Statistical Modeling, Analysis and Management of Fuzzy Data," or SMFD for short, is an important contribution to a better understanding of a basic issue -an issue which has been controversial, and still is though to a lesser degree. In substance, the issue is: are fuzziness and randomness distinct.

1. Fuzziness and Randomness.- Fuzziness and randomness.- 2. Fuzzy-Valued Random Elements.- On the variance of random fuzzy variables.- f-inequality indices for fuzzy random variables. Often the book that recommended for your requirements is Statistical Modeling, Analysis and Management of Fuzzy Data Studies in Fuzziness and Soft Computing this book consist a lot of the information of the condition of this world now. This particular book was. Get this from a library! Statistical modeling, analysis and management of fuzzy data. [Carlo Bertoluzza; María Á Gil; D A Ralescu;] -- The contributions in this book connect Probability Theory/Statistics and Fuzzy Set Theory in different ways. Some of these connections are either philosophical or theoretical in nature, but most of. The series “Studies in Fuzziness and Soft Computing” contains publications on various topics in the area of soft computing, which include fuzzy sets, rough sets, neural networks, evolutionary computation, probabilistic and evidential reasoning, multi-valued logic, and related fields. Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data.

The fuzzy data envelop analysis algorithm can be used to decrease or avoid the influence of subjectivity on the analysis results Puri and Yadav, 2014, Wang and Chin, 2011. An analysis framework is applied to extract a series of product data, which is considered as the foundation of failure analysis Kang et al., 2017. Jun 01, 2016 · 3. Statistical analysis with soft computing 3.1. Questionnaire with fuzzy set theory. Regarding the choice of statistical tools in the evaluation, and because the evaluation must align with the same logic base, significant differences should exist on the metrics. Statistical modeling, analysis and management of fuzzy data. [Carlo Bertoluzza; María Á Gil; D A Ralescu;]. Statistics and fuzzy data analysis. Series Title: Studies in fuzziness and soft computing, v. 87.Studies in fuzziness and soft computing. Bronevich A.G., Karkishchenko A.N. 2002 Statistical classes and fuzzy set theoretical classification of probability distributions. In: Bertoluzza C., Gil MÁ., Ralescu D.A. eds Statistical Modeling, Analysis and Management of Fuzzy Data. Studies in Fuzziness and Soft Computing, vol. The contributions in this book state the complementary rather than competitive relationship between Probability and Fuzzy Set Theory and allow solutions to real life problems with suitable combinations of.

application of fuzzy techniques in statistical analysis, the need is felt for a more systematic insight into the potentialities of cross fertilization between Statistics and Fuzzy Logic. In line. statistical models to model the data. In the current study, fuzzy logic is used to simplify the entire process of decision-making process. Simple rules are generated from fuzzy decision tree which describe the pavement performance prediction. A separate method based on logistic regression is then developed for comparing the results obtained.

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