Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1998
|
| Schriftenreihe: | International Series in Intelligent Technologies
11 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource (XXIV, 371 p) |
| ISBN: | 9781461554738 9781461375005 |
| ISSN: | 1382-3434 |
| DOI: | 10.1007/978-1-4615-5473-8 |
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| id | DE-604.BV042420906 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:07Z |
| institution | BVB |
| isbn | 9781461554738 9781461375005 |
| issn | 1382-3434 |
| language | English |
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| spelling | Ayyub, Bilal M. Verfasser aut Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach by Bilal M. Ayyub, Madan M. Gupta Boston, MA Springer US 1998 1 Online-Ressource (XXIV, 371 p) txt rdacontent c rdamedia cr rdacarrier International Series in Intelligent Technologies 11 1382-3434 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 Computer science Artificial intelligence Logic, Symbolic and mathematical Mathematical optimization Operations research Computer Science Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Calculus of Variations and Optimal Control; Optimization Operation Research/Decision Theory Informatik Künstliche Intelligenz Gupta, Madan M. Sonstige oth https://doi.org/10.1007/978-1-4615-5473-8 Verlag Volltext |
| spellingShingle | Ayyub, Bilal M. Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach Computer science Artificial intelligence Logic, Symbolic and mathematical Mathematical optimization Operations research Computer Science Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Calculus of Variations and Optimal Control; Optimization Operation Research/Decision Theory Informatik Künstliche Intelligenz |
| title | Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach |
| title_auth | Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach |
| title_exact_search | Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach |
| title_full | Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach by Bilal M. Ayyub, Madan M. Gupta |
| title_fullStr | Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach by Bilal M. Ayyub, Madan M. Gupta |
| title_full_unstemmed | Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach by Bilal M. Ayyub, Madan M. Gupta |
| title_short | Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach |
| title_sort | uncertainty analysis in engineering and sciences fuzzy logic statistics and neural network approach |
| topic | Computer science Artificial intelligence Logic, Symbolic and mathematical Mathematical optimization Operations research Computer Science Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Calculus of Variations and Optimal Control; Optimization Operation Research/Decision Theory Informatik Künstliche Intelligenz |
| topic_facet | Computer science Artificial intelligence Logic, Symbolic and mathematical Mathematical optimization Operations research Computer Science Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Calculus of Variations and Optimal Control; Optimization Operation Research/Decision Theory Informatik Künstliche Intelligenz |
| url | https://doi.org/10.1007/978-1-4615-5473-8 |
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