Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach / by Bilal M. Ayyub, Madan M. Gupta
(International Series in Intelligent Technologies ; 11)
| データ種別 | 電子ブック |
|---|---|
| 出版者 | Boston, MA : Springer US : Imprint: Springer |
| 出版年 | 1998 |
| 本文言語 | 英語 |
| 大きさ | XXIV, 371 p : online resource |
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| 内容注記 | 1. The Role of Constrained Fuzzy Arithmetic in Engineering 2. General Perspective on the Formalization of Uncertain Knowledge 3. Distributional Representations of Random Interval Measurements 4. A Fuzzy Morphology: a Logical Approach 5. Reliability Analysis with Fuzziness and Randomness 6. Fuzzy Signal Detection with Multiple Waveform Features 7. Uncertainty Modeling of Normal Vibrations 8. Modeling and Implementation of Fuzzy Time Point Reasoning in Microprocessor Systems 9. Model Learning with Bayesian Networks for Target Recognition 10. System Life Cycle Optimization Under Uncertainty 11. Valuation-Based Systems for Pavement Management Decision Making 12. Hybrid Least-Square Regression Analysis 13. Linear Regression with Random Fuzzy Numbers 14. Neural Net Solutions to Systems of Fuzzy Linear Equations 15. Fuzzy Logic: A Case Study in Performance Measurement 16. Fuzzy Genetic Algorithm Based Approach to Machine Learning Under Uncertainty 17. Recurrent Neuro-Fuzzy Models of Complex Systems 18. Adaptive Fuzzy Systems with Sinusoidal Membership Functions 19. A Computational Method for Fuzzy Optimization 20. Interaction of Fuzzy Knowledge Granules for Conjunctive Logic 21. Fuzzy Decision Processes with Expected Fuzzy Rewards 22. On the Computability of Possibilistic Reliability 23. Distributed Reasoning with Uncertain Data 24. A Fresh Perspective on Uncertainty Modeling: Uncertainty vs. Uncertainty Modeling About the Editors |
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| 一般注記 | 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 |
| 著者標目 | *Ayyub, Bilal M. author Gupta, Madan M. author SpringerLink (Online service) |
| 件 名 | LCSH:Computer science LCSH:Operations research LCSH:Decision making LCSH:Artificial intelligence LCSH:Mathematical logic LCSH:Calculus of variations FREE:Computer Science FREE:Artificial Intelligence (incl. Robotics) FREE:Mathematical Logic and Foundations FREE:Calculus of Variations and Optimal Control; Optimization FREE:Operation Research/Decision Theory |
| 分 類 | DC23:006.3 |
| 巻冊次 | ISBN:9781461554738 
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| ISBN | 9781461554738 |
| URL | http://dx.doi.org/10.1007/978-1-4615-5473-8 |
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