© 1998

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


Part of the International Series in Intelligent Technologies book series (ISIT, volume 11)

Table of contents

  1. Front Matter
    Pages i-xxiv
  2. Uncertainty Types, Models, and Measures

  3. Applications to Engineering Systems

  4. Fuzzy-Neuro Data Analysis and Forecasting

    1. Yun-Hsi Oscar Chang, Bilal M. Ayyub
      Pages 179-191
    2. Wolfgang. Näther, Ralf. Körner
      Pages 193-212
    3. James. J. Buckley, Thomas. Feuring, Yoichi. Hayashi
      Pages 213-232
    4. Salwa H. Ammar, Ronald H. Wright
      Pages 233-245
    5. I. Burak Özyurt, Lawrence O. Hall
      Pages 247-258
  5. Fuzzy-Neuro Systems

    1. Can Işik, Mohammad Farrokhi, Jiann-Horng Lin, A. Mete Çakmakci
      Pages 259-271
    2. Liang Jin, Madan M. Gupta
      Pages 273-289
  6. Fuzzy Decision Making and Optimization

    1. Weldon A. Lodwick, K. David Jamison
      Pages 291-300

About this book


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

Authors and affiliations

  1. 1.University of MarylandCollege ParkUSA
  2. 2.University of SaskatchewanSaskatoonCanada

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