Titlebook: Extremal Fuzzy Dynamic Systems; Theory and Applicati Gia Sirbiladze Book 2013 Springer Science+Business Media, LLC 2013 Dynamic Systems The

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Identification of Fuzzy-Integral Models of Extremal Fuzzy Processesilistic uncertainty, the source of which is extremal fuzzy time intervals (.). The regularization conditions for obtaining quasi-optimal estimates are substantiated by the proved theorems. Corresponding algorithms are provided. The results obtained are illustrated by examples in the case of a finite set of EFDS states.

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Summary of Chapters 3–8ilistic) uncertainty is an expert or an object–expert combination, this book presents an approach in which the source of uncertainty is not only an object–expert combination, but time, too. That is, the time factor also contributes to the uncertainty in dynamic systems, which makes the range of problems to be investigated much wider.

Meditative

Mojtaba Heydari,Fahimeh Marefatic operations (mutation and crossover) are performed on them. Finally, a new population is created. After this, the GA runs on the newly created population, and the process continues until some exit criterion is satisfied. We shall assume that the reader is familiar with the fundamental concepts of genetic algorithms.

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On the Genetic Algorithms Approach and Software Libraryic operations (mutation and crossover) are performed on them. Finally, a new population is created. After this, the GA runs on the newly created population, and the process continues until some exit criterion is satisfied. We shall assume that the reader is familiar with the fundamental concepts of genetic algorithms.

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CARE

Funding Innovation and Entrepreneurship,ne measures are defined. The notions of extremal fuzzy time moments and intervals are introduced, and their monotone algebraic structures, which form the most important part of the fuzzy instrument of modeling extremal fuzzy dynamic systems, are discussed.

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HUMID

Melodrama

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