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

1. Front Matter

   Pages i-xxiv

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2. Uncertainty Types, Models, and Measures

   1. The Role of Constrained Fuzzy Arithmetic in Engineering

      • George J. Klir

      Pages 1-19

   2. General Perspective on the Formalization of Uncertain Knowledge

      • Elisabeth Umkehrer, Kerstin Schill

      Pages 21-35

   3. Distributional Representations of Random Interval Measurements

      • Cliff Joslyn

      Pages 37-51

   4. A Fuzzy Morphology: a Logical Approach

      • Bernard De Baets

      Pages 53-67

3. Applications to Engineering Systems

   1. Reliability Analysis With Fuzziness and Randomness

      • Ru-Jen Chao, Bilal M. Ayyub

      Pages 69-79

   2. Fuzzy Signal Detection With Multiple Waveform Features

      • J. R. Boston

      Pages 81-95

   3. Uncertainty Modeling of Normal Vibrations

      • Matthias Kudra

      Pages 97-107

   4. Modeling and Implementation of Fuzzy Time Point Reasoning in Microprocessor Systems

      • S. M. Yuen, K. P. Lam

      Pages 109-125

   5. Model Learning With Bayesian Networks for Target Recognition

      • Jun Liu, Kuo-Chu Chang

      Pages 127-141

   6. System Life Cycle Optimization Under Uncertainty

      • Odd Andreas Asbjornsen

      Pages 143-156

   7. Valuation-Based Systems for Pavement Management Decision Making

      • Nii O. Attoh-Okine

      Pages 157-177

4. Fuzzy-Neuro Data Analysis and Forecasting

   1. Hybrid Least-Squares Regression Analysis

      • Yun-Hsi Oscar Chang, Bilal M. Ayyub

      Pages 179-191

   2. Linear Regression With Random Fuzzy Numbers

      • Wolfgang. Näther, Ralf. Körner

      Pages 193-212

   3. Neural Net Solutions to Systems of Fuzzy Linear Equations

      • James. J. Buckley, Thomas. Feuring, Yoichi. Hayashi

      Pages 213-232

   4. Fuzzy Logic: a Case Study in Performance Measurement

      • Salwa H. Ammar, Ronald H. Wright

      Pages 233-245

   5. Fuzzy Genetic Algorithm Based Approach to Machine Learning Under Uncertainty

      • I. Burak Özyurt, Lawrence O. Hall

      Pages 247-258

5. Fuzzy-Neuro Systems

   1. Recurrent Neuro-Fuzzy Models of Complex Systems

      • Can Işik, Mohammad Farrokhi, Jiann-Horng Lin, A. Mete Çakmakci

      Pages 259-271

   2. Adaptive Fuzzy Systems With Sinusoidal Membership Functions

      • Liang Jin, Madan M. Gupta

      Pages 273-289

6. Fuzzy Decision Making and Optimization

   1. A Computational Method for Fuzzy Optimization

      • Weldon A. Lodwick, K. David Jamison

      Pages 291-300

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.

• Analysis
• Bayesian network
• Cyc
• algorithms
• cognition
• complex system
• genetic algorithm
• knowledge
• learning
• machine learning
• modeling
• neural network
• optimization
• simulation
• uncertainty

• University of Maryland, College Park, USA

  Bilal M. Ayyub

• University of Saskatchewan, Saskatoon, Canada

  Madan M. Gupta

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