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Uncertainty Analysis in Engineering and Sciences : Fuzzy Logic, Statistics, and Neural Network Approach - Bilal M. Ayyub

Uncertainty Analysis in Engineering and Sciences

Fuzzy Logic, Statistics, and Neural Network Approach

By: Bilal M. Ayyub (Editor), Madan M. Gupta (Editor)

Hardcover

Published: 31st October 1997
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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.

Contributors
Foreword
Preface
Uncertainty Types, Models, and Measures
The Role of Constrained Fuzzy Arithmetic in Engineeringp. 1
General Perspective on the Formalization of Uncertain Knowledgep. 21
Distributional Representations of Random Interval Measurementsp. 37
A Fuzzy Morphology: a Logical Approachp. 53
Applications to Engineering Systems
Reliability Analysis with Fuzziness and Randomnessp. 69
Fuzzy Signal Detection with Multiple Waveform Featuresp. 81
Uncertainty Modeling of Normal Vibrationsp. 97
Modeling and Implementation of Fuzzy Time Point Reasoning in Microprocessor Systemsp. 109
Model Learning with Bayesian Networks for Target Recognitionp. 127
System Life Cycle Optimization Under Uncertaintyp. 143
Valuation-Based Systems for Pavement Management Decision Makingp. 157
Fuzzy-Neuro Data Analysis and Forecasting
Hybrid Least-Square Regression Analysisp. 179
Linear Regression with Random Fuzzy Numbersp. 193
Neural Net Solutions to Systems of Fuzzy Linear Equationsp. 213
Fuzzy Logic: A Case Study in Performance Measurementp. 233
Fuzzy Genetic Algorithm Based Approach to Machine Learning Under Uncertaintyp. 247
Fuzzy-Neuro Systems
Recurrent Neuro-Fuzzy Models of Complex Systemsp. 259
Adaptive Fuzzy Systems with Sinusoidal Membership Functionsp. 273
Fuzzy Decision Making and Optimization
A Computational Method for Fuzzy Optimizationp. 291
Interaction of Fuzzy Knowledge Granules for Conjunctive Logicp. 301
Fuzzy Decision Processes with Expected Fuzzy Rewardsp. 313
On the Computability of Possibilistic Reliabilityp. 325
Distributed Reasoning with Uncertain Datap. 339
A Fresh Perspective on Uncertainty Modeling: Uncertainty vs. Uncertainty Modelingp. 353
Subject Indexp. 365
About the Editorsp. 371
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9780792380306
ISBN-10: 0792380304
Series: International Series in Intelligent Technologies
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 371
Published: 31st October 1997
Publisher: Springer
Country of Publication: NL
Dimensions (cm): 23.5 x 15.5  x 3.18
Weight (kg): 1.62