Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach

Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach
Author :
Publisher : Springer Science & Business Media
Total Pages : 414
Release :
ISBN-10 : 0792380304
ISBN-13 : 9780792380306
Rating : 4/5 (306 Downloads)

Book Synopsis Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach by : Bilal Ayyub

Download or read book Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach written by Bilal Ayyub and published by Springer Science & Business Media. This book was released on 1997-10-31 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty in a rather narrow sense since the beginning of this century. In engineering and uncertainty has for a long time been considered as in sciences, however, synonymous with random, stochastic, statistic, or probabilistic. Only since the early sixties views on uncertainty have ~ecome more heterogeneous and more tools to model uncertainty than statistics have been proposed by several scientists. The problem of modeling uncertainty adequately has become more important the more complex systems have become, the faster the scientific and engineering world develops, and the more important, but also more difficult, forecasting of future states of systems have become. The first question one should probably ask is whether uncertainty is a phenomenon, a feature of real world systems, a state of mind or a label for a situation in which a human being wants to make statements about phenomena, i. e. , reality, models, and theories, respectively. One cart also ask whether uncertainty is an objective fact or just a subjective impression which is closely related to individual persons. Whether uncertainty is an objective feature of physical real systems seems to be a philosophical question. This shall not be answered in this volume.


Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach Related Books

Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach
Language: en
Pages: 414
Authors: Bilal Ayyub
Categories: Computers
Type: BOOK - Published: 1997-10-31 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

Uncertainty has been of concern to engineers, managers and . scientists for many centuries. In management sciences there have existed definitions of uncertainty
Uncertainty Modeling for Engineering Applications
Language: en
Pages: 0
Authors: Flavio Canavero
Categories: Technology & Engineering
Type: BOOK - Published: 2019-01-16 - Publisher: Springer

DOWNLOAD EBOOK

This book provides an overview of state-of-the-art uncertainty quantification (UQ) methodologies and applications, and covers a wide range of current research,
The Uncertainty Analysis of Model Results
Language: en
Pages: 346
Authors: Eduard Hofer
Categories: Mathematics
Type: BOOK - Published: 2018-07-01 - Publisher: Springer

DOWNLOAD EBOOK

This book is a practical guide to the uncertainty analysis of computer model applications. Used in many areas, such as engineering, ecology and economics, compu
Mathematics of Uncertainty Modeling in the Analysis of Engineering and Science Problems
Language: en
Pages: 442
Authors: Chakraverty, S.
Categories: Mathematics
Type: BOOK - Published: 2014-01-31 - Publisher: IGI Global

DOWNLOAD EBOOK

"This book provides the reader with basic concepts for soft computing and other methods for various means of uncertainty in handling solutions, analysis, and ap
Uncertainty
Language: en
Pages: 274
Authors: William Briggs
Categories: Mathematics
Type: BOOK - Published: 2016-07-15 - Publisher: Springer

DOWNLOAD EBOOK

This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, wh