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Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach / Edition 1

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Overview

Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach examines the use of newly developed analytical tools for studying uncertainty analysis in engineering, control systems, and the sciences. It is the work of 38 experts who have written chapters on newly developed analytical methods - fuzzy logic, neural networks, simulation, and Bayesian techniques - and have applied them to uncertainty phenomena arising out of information and knowledge problems in the fields of engineering and the sciences.
The book is divided into the following parts: Part I reports the theoretical studies on uncertainty types, models and measures; Part II reviews the applications of uncertain theoretical tools to engineering systems; Part III describes the methodologies of fuzzy-neural data analysis and forecasting; Part IV presents two chapters on fuzzy-neuro systems; and Part V describes the methodologies for fuzzy decision making and optimization and their computational methods.
The Editors provide a concluding chapter on uncertainty and uncertainty modeling. This is a carefully developed book that treats the topic of uncertainty from fresh perspectives and in depth.
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Editorial Reviews

Booknews
Examines the use of newly developed analytical tools such as fuzzy logic, neural networks, simulation, and Bayesian techniques for studying uncertainty analysis in engineering, control systems, and the sciences. Part I reports on theoretical studies of uncertainty types, models, and measures, and Part II reviews applications of theoretical tools to engineering systems. Parts III and IV describe methodologies of fuzzy-neural data analysis and forecasting, and discuss fuzzy- neuro systems. Part V describes methodologies for fuzzy decision making and optimization and their computational methods. A concluding chapter explores uncertainty modeling. Based on papers from a September 1995 conference. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

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. Näther, R. Körner. 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. Özyurt, L.O. Hall. IV: Fuzzy-Neuro Systems. 17. Recurrent Neuro-Fuzzy Models of Complex Systems; C. Işik, 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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