Uncertainty Forecasting in Engineering - Rilegato

Moller, Bernd; Reuter, Uwe

Rilegato

ISBN 10: 3540371737 ISBN 13: 9783540371731

Casa editrice: Springer Nature, 2007

Riassunto

Observations of uncertainty in measured data with time improves forecasting capability in a wide range of fields in engineering. This book provides an introduction to uncertainty forecasting based on fuzzy time series. It details descriptive, modeling, and forecasting methods for fuzzy time series. Coverage places emphasis on forecasting based on fuzzy random processes as well as forecasting involving fuzzy neuronal networks.

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Informazioni sull'autore

Univ.-Prof. Dr.-Ing. habil. Bernd Möller (*1941) studied civil engineering. From 1994 –1996 he was professor for Numerical Methods and Computer Science in Engineering at the University of Braunschweig, and from 1996 – 2006 professor for Solid and Structural Mechanics at the University of Technology in Dresden, Germany. Professor Möller has been retired since 2006.

Dr.-Ing. Uwe Reuter (*1979) studied civil engineering. From 2003 – 2007 he was research associate at the Institute of Statics and Dynamics of Structures at the University of Technology in Dresden, Germany. Today he is head of the Computing Center at the Faculty of Civil Engineering at the University of Technology in Dresden.

Dalla quarta di copertina

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.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2007
• Lingua: Inglese
• ISBN 10: 3540371737
• ISBN 13: 9783540371731
• Rilegatura: Copertina rigida
• Numero di pagine: 202
• Contatto del produttore: non disponibile
• Persona responsabile: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Germania