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

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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.
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Product details

  • Hardback | 371 pages
  • 154.9 x 236.2 x 27.9mm | 589.68g
  • Dordrecht, Netherlands
  • English
  • 1998 ed.
  • XXIV, 371 p.
  • 0792380304
  • 9780792380306

Table of contents

Foreword; H.-J. Zimmermann. Preface; B.M. Ayyub, M.M. Gupta. I: Uncertainty Types, Models, and Measures. 1. The Role of Constrained Fuzzy Arithmetic in Engineering; G.J. Klir. 2. General Perspective on the Formalization of Uncertain Knowledge; E. Umkehrer, K. Schill. 3. Distributional Representations of Random Interval Measurements; C. Joslyn. 4. A Fuzzy Morphology: A Logical Approach; B. de Baets. II: Applications to Engineering Systems. 5. Reliability Analysis with Fuzziness and Randomness; Ru-Jen Chao, B.M. Ayyub. 6. Fuzzy Signal Detection with Multiple Waveform Features; J.R. Boston. 7. Uncertainty Modeling of Normal Vibrations; M. Kudra. 8. Modeling and Implementation of Fuzzy Time Point Reasoning in Microprocessor Systems; S.M. Yuen, K.P. Lam. 9. Model Learning with Bayesian Networks for Target Recognition; Jun Liu, Kuo-Chu Chang. 10. System Life Cycle Optimization Under Uncertainty; O.A. Asbjornsen. 11. Valuation-Based Systems for Pavement Management Decision Making; N.O. Attoh-Okine. III: Fuzzy-Neuro Data Analysis and Forecasting. 12. Hybrid Least- Square Regression Analysis; Yun-Hsi O. Chang, B.M. Ayyub. 13. Linear Regression with Random Fuzzy Numbers; W. Nather, R. Koerner. 14. Neural Net Solutions to Systems of Fuzzy Linear Equations; J.J. Buckley, et al. 15. Fuzzy Logic: A Case Study in Performance Measurement; S. Ammar, R. Wright. 16. Fuzzy Genetic Algorithm Based Approach to Machine Learning Under Uncertainty; I.B.OEzyurt, L.O. Hall. IV: Fuzzy-Neuro Systems. 17. Recurrent Neuro-Fuzzy Models of Complex Systems; C. I ik, et al. 18. Adaptive Fuzzy Systems with Sinusoidal Membership Functions; Liang Jin, M.M. Gupta. V: Fuzzy Decision Making and Optimization. 19. A Computational Method for Fuzzy Optimization; W.A. Lodwick, K.D. Jamison. 20. Interaction of Fuzzy Knowledge Granules for Conjunctive Logic; T. Whalen. 21. Fuzzy Decision Processes with Expected Fuzzy Rewards; Y. Yoshida. 22. On the Computability of Possibilistic Reliability; B. Cappelle, E.E. Kerre. 23. Distributed Reasoning with Uncertain Data; K. Schill. 24. A Fresh Perspective on Uncertainty Modeling: Uncertainty vs. Uncertainty Modeling; H.-J. Zimmermann. Subject Index. About the Editors.
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