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.
Author(s): Bilal M. Ayyub PhD, PE, Madan M. Gupta PhD (auth.)
Series: International Series in Intelligent Technologies 11
Edition: 1
Publisher: Springer US
Year: 1998
Language: English
Pages: 371
Tags: Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Calculus of Variations and Optimal Control; Optimization; Operation Research/Decision Theory
Front Matter....Pages i-xxiv
The Role of Constrained Fuzzy Arithmetic in Engineering....Pages 1-19
General Perspective on the Formalization of Uncertain Knowledge....Pages 21-35
Distributional Representations of Random Interval Measurements....Pages 37-51
A Fuzzy Morphology: a Logical Approach....Pages 53-67
Reliability Analysis With Fuzziness and Randomness....Pages 69-79
Fuzzy Signal Detection With Multiple Waveform Features....Pages 81-95
Uncertainty Modeling of Normal Vibrations....Pages 97-107
Modeling and Implementation of Fuzzy Time Point Reasoning in Microprocessor Systems....Pages 109-125
Model Learning With Bayesian Networks for Target Recognition....Pages 127-141
System Life Cycle Optimization Under Uncertainty....Pages 143-156
Valuation-Based Systems for Pavement Management Decision Making....Pages 157-177
Hybrid Least-Squares Regression Analysis....Pages 179-191
Linear Regression With Random Fuzzy Numbers....Pages 193-212
Neural Net Solutions to Systems of Fuzzy Linear Equations....Pages 213-232
Fuzzy Logic: a Case Study in Performance Measurement....Pages 233-245
Fuzzy Genetic Algorithm Based Approach to Machine Learning Under Uncertainty....Pages 247-258
Recurrent Neuro-Fuzzy Models of Complex Systems....Pages 259-271
Adaptive Fuzzy Systems With Sinusoidal Membership Functions....Pages 273-289
A Computational Method for Fuzzy Optimization....Pages 291-300
Interaction of Fuzzy Knowledge Granules for Conjunctive Logic....Pages 301-311
Fuzzy Decision Processes With Expected Fuzzy Rewards....Pages 313-323
On the Computability of Possibilistic Reliability....Pages 325-337
Distributed Reasoning With Uncertain Data....Pages 339-351
A Fresh Perspective on Uncertainty Modeling: Uncertainty Vs. Uncertainty Modeling....Pages 353-364
Back Matter....Pages 365-371
