Uncertainty Forecasting in Engineering
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Beschreibung
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.
Forecasting is fascinating. Who wouldn’t like to cast a glimpse into the future? Far removed from metaphysics, mathematical methods such as time-lapse techniques, time series or arti?cial neural netwoks o?er a rational means of achieving this. A precondition for the latter is the availability of a sequence of observed values from the past whose temporal classi?cation permits the deduction of attributes necessary for forecasting purposes. The subject matter of this book is uncertain forecasting using time series and neural networks based on uncertain observed data. ‘Uncertain’ data - plies information exhibiting inaccuracy, uncertainty and questionability. The uncertainty of individual observations is modeled in this book by fuzziness. Sequences of uncertain observations hence constitute fuzzy time series. By means of new discretization techniques for uncertain data it is now possible to correctly and completely retain data uncertainty in forecasting work. The book presents numerical methods which permit successful forecasting not only in engineering but also in many other ?elds such as environmental science or economics, assuming of course that a suitable sequence of observed data is available. By taking account of data uncertainty, the indiscriminate reduction of uncertain observations to real numbers is avoided. The larger information content described by uncertainty is retained, and compared with real data, provides a deeper insight into causal relationships. This in turn has practical consequences as far as the full?lment of technical requirements in engineering applications is concerned.
Fuzzy time series can be applied in many fields in engineering like environmental engineering or civil engineering Two simulation-based important forecasting strategies are explained: forecasting based on fuzzy-ARMA-processes or fuzzy-white-noise-processes and forecasting based on fuzzy artificial neural networks A complete new description of uncertain data as incremental fuzzy data is given Includes supplementary material: sn.pub/extras
Autor*in
Bernd Möller
Themen in »Uncertainty Forecasting in Engineering«
Forecasting Fuzzy Data Fuzzy artificial neutral networks Measure Random Values Time series Uncertainty artificial neural network fuzzy
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From the reviews:
"The authors deal with a new and fascinating subject: forcasting the incertainty in civil engineering and environmental science. … the volume is a scientific monograph and represents a valuable contribution to the field. It is intended for civil engineers as well as to many professionals working in related fields." (Petre P. Teodorescu, Zentralblatt MATH, Vol. 1131 (9), 2008)
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