Ebook: Fuzzy Logic and Probability Applications: A Practical Guide
- Genre: Mathematics // Probability
- Series: ASA-SIAM Series on Statistics and Applied Probability
- Year: 2002
- Publisher: Society for Industrial Mathematics
- Language: English
- djvu
Fuzzy Logic and Probability Applications: Bridging the Gap makes an honest effort to show both the shortcomings and benefits of each technique, and even demonstrates useful combinations of the two. It provides clear descriptions of both fuzzy logic and probability, as well as the theoretical background, examples, and applications from both fields, making it a useful hands-on workbook for members of both camps. It contains enough theory and references to fundamental work to provide firm ground for both engineers and scientists at the undergraduate level and above. Readers should have a familiarity with mathematics through calculus.
Use of this book is not restricted to a specific course or application. It can be used in teaching probability, fuzzy logic, general problem solving, or in any course in which probability and fuzzy logic are not normally taught together. It has applications in control theory and artificial intelligence, knowledge acquisition/management, and risk/reliability analysis.
Contents Foreword by Lotfi A. Zadeh; Foreword by Patrick Suppes; Preface; Part I: Fundamentals; Chapter 1: Introduction; Chapter 2: Fuzzy Set Theory, Fuzzy Logic, and Fuzzy Systems; Chapter 3: Probability Theory; Chapter 4: Bayesian Methods; Chapter 5: Considerations for Using Fuzzy Set Theory and Probability Theory; Chapter 6: Guidelines for Eliciting Expert Judgment as Probabilities or Fuzzy Logic; Part II: Applications; Chapter 7: Image Enhancement: Probability Versus Fuzzy Expert Systems; Chapter 8: Engineering Process Control; Chapter 9: Structural Safety Analysis: A Combined Fuzzy and Probability Approach; Chapter 10: Aircraft Integrity and Reliability; Chapter 11: Auto Reliability Project; Chapter 12: Control Charts for Statistical Process Control; Chapter 13: Fault Tree Logic Models; Chapter 14: Uncertainty Distributions Using Fuzzy Logic; Chapter 15: Signal Validation Using Bayesian Belief Networks and Fuzzy Logic; Index.