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|a 9783540371762
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| 100 |
1 |
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|a Möller, Bernd
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| 245 |
0 |
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|a Uncertainty Forecasting in Engineering
|h Elektronische Ressource
|c by Bernd Möller, Uwe Reuter
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| 250 |
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|a 1st ed. 2007
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2007, 2007
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| 300 |
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|a XIV, 202 p
|b online resource
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| 505 |
0 |
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|a Mathematical Description of Uncertain Data -- Analysis of Time Series Comprised of Uncertain Data -- Forecasting of Time Series Comprised of Uncertain Data -- Uncertain Forecasting in Engineering and Environmental Science
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| 653 |
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|a Environmental monitoring
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| 653 |
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|a Mechanics, Applied
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| 653 |
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|a Statistics
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| 653 |
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|a Probability Theory
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| 653 |
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|a Buildings / Design and construction
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| 653 |
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|a Engineering Mechanics
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| 653 |
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|a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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| 653 |
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|a Environmental Monitoring
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| 653 |
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|a Building Construction and Design
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| 653 |
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|a Probabilities
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| 700 |
1 |
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|a Reuter, Uwe
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
0 |
|a 10.1007/978-3-540-37176-2
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-540-37176-2?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 519
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| 520 |
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|a This book deals with uncertainty forecasting based on a fuzzy time series approach, including fuzzy random processes and artificial neural networks. A consideration of data and measurement uncertainty enhances forecasting in a wide range of applications, particularly in the fields of engineering, environmental science and civil engineering. Uncertain data are described by means of a new incremental fuzzy representation which permits a complete and accurate estimation of uncertainty. The book is aimed at engineers as well as professionals working in related fields. Descriptive, modeling and forecasting methods pertaining to fuzzy time series are introduced and explained in detail. Emphasis is placed on forecasting with the aid of fuzzy random processes, such as fuzzy ARMA processes and fuzzy white-noise processes, as well as forecasting based on artificial neural networks. All numerical algorithms are comprehensively described and demonstrated by way of practical examples
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