Uncertainty Forecasting in Engineering [electronic resource] /
Möller, Bernd.author.
Reuter, Uwe.author.
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.
Description based upon print version of record.
Includes bibliographical references (p. [193]-197) and index.
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.
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.
Statistics .
Mechanics.
Mechanics, Applied.
Environmental monitoring.
Architecture.
Building.
Engineering.
Probabilities.
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Theoretical and Applied Mechanics.
Monitoring/Environmental Analysis.
Building Construction and Design.
Probability Theory and Stochastic Processes.