Uncertainty theory is a branch of mathematics based on normality, monotonicity, self-duality, countable subadditivity, and product measure axioms. Uncertainty is any concept that satisfies the axioms of uncertainty theory. Thus uncertainty is neither randomness nor fuzziness. It is also known from some surveys that a lot of phenomena do behave like uncertainty. How do we model uncertainty? How do we use uncertainty theory? In order to answer these questions, this book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory, including uncertain programming, uncertain risk analysis, uncertain reliability analysis, uncertain process, uncertain calculus, uncertain differential equation, uncertain logic, uncertain entailment, and uncertain inference. Mathematicians, researchers, engineers, designers, and students in the field of mathematics, information science, operations research, system science, industrial engineering, computer science, artificial intelligence, finance, control, and management science will find this work a stimulating and useful reference.
| Serija: | Studies in Computational Intelligence |
| Leidėjas: | Springer Berlin Heidelberg |
| Išleidimo metai: | 2014 |
| Knygos puslapių skaičius: | 364 |
| ISBN-10: | 3642422489 |
| ISBN-13: | 9783642422485 |
| Formatas: | 235 x 155 x 20 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
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