PRZEDMIOTEM OFERTY JEST KOD DOSTĘPOWY DO KSIĄŻKI ELEKTRONICZNEJ (EBOOK)
KSIĄŻKA JEST DOSTĘPNA NA ZEWNĘTRZNEJ PLATFORMIE. KSIĄŻKA NIE JEST W POSTACI PLIKU.
This book presents in a systematic and comprehensive manner the modeling of uncertainty, vagueness, or imprecision, alias "fuzziness," in just about any field of science and engineering. It delivers a usable methodology for modeling in the absence of real-time feedback. The book includes a short introduction to fuzzy logic containing basic definitions of fuzzy set theory and fuzzy rule systems. It describes methods for the assessment of rule systems, systems with discrete response sets, for modeling time series, for exact physical systems, examines verification and redundancy issues, and investigates rule response functions. Definitions and propositions, some of which have not been published elsewhere, are provided; numerous examples as well as references to more elaborate case studies are also given. Fuzzy rule-based modeling has the potential to revolutionize fields such as hydrology because it can handle uncertainty in modeling problems too complex to be approached by a stochastic analysis. There is also excellent potential for handling large-scale systems such as regionalization or highly non-linear problems such as unsaturated groundwater pollution.
- Autorzy: Andras Bardossy Lucien Duckstein
- Wydawnictwo: Taylor & Francis
- Data wydania: 2022
- Wydanie: 1
- Liczba stron:
- Forma publikacji: ePub (online)
- Język publikacji: angielski
- ISBN: 9780429605345
BRAK MOŻLIWOŚCI POBRANIA PLIKU. Drukowanie: OGRANICZENIE DO 2 stron. Kopiowanie: OGRANICZENIE DO 2 stron.
- Cover
- Half Title
- Series
- Title
- Copyright
- Preface
- Acknowledgments
- Dedication
- Contents
- 1 Introduction
- 2 Basic elements and definitions
- 2.1 Fuzzy sets: definitions and properties
- 2.1.1 Membership functions
- 2.1.2 Operations on fuzzy sets
- 2.1.3 Linguistic variables
- 2.1.4 Linguistic modifiers
- 2.2 Fuzzy numbers
- 2.2.1 Operations on fuzzy numbers
- 2.2.2 Fuzzy mean and median
- 2.2.3 Distance between fuzzy numbers
- 2.3 Assessment of the membership functions
- 2.4 Fuzzy sets, possibilities and probabilities
- 2.4.1 Possibility
- 2.4.2 Membership functions versus probabilities
- 3. Fuzzy rules
- 3.1 The structure of a fuzzy rule
- 3.1.1 The form of a fuzzy rule
- 3.1.2 Implication operators
- 3.1.3 Degree of fulfillment of a fuzzy rule
- 3.2 Combination of fuzzy rule responses
- 3.2.1 Minimum combinations
- 3.2.2 Maximum combinations
- 3.2.3 Additive combinations
- 3.2.4 Properties of the combination methods
- 3.2.5 Other combination methods
- 3.2.6 Comparison of the combination methods
- 3.3 Defuzzification
- 3.3.1 Defuzzification methods
- 3.4 Case of fuzzy premises
- 3.4.1 Fuzzy premises crisp response
- 3.4.2 Fuzzy premises fuzzy responses
- 3.5 Rules with multiple responses
- 4 Rule systems
- 4.1 Completeness and redundancy
- 4.1.1 Completeness
- 4.1.2 Redundancy
- 4.2 Variables to be used for rule systems
- 4.2.1 Hybrid rule systems
- 4.2.2 Interaction of variables
- 4.3 Rules and continuous functions
- 4.3.1 Continuity of rule system response functions
- 4.3.2 Smoothness of rule system response functions
- 4.4 Membership functions in rule systems
- 4.4.1 Response membership functions
- 4.4.2 Argument membership functions
- 4.5 Sensitivity of the response functions
- 5 Rule construction
- 5.1 Explicit rule specification
- 5.2 Deriving rule systems from datasets
- 5.3 Known rule structure
- 5.3.1 The training set
- 5.3.2 The counting algorithm
- 5.3.3 The weighted counting algorithm
- 5.3.5 Comparison of the algorithms
- 5.4 Partially explicit rule structures
- 5.4.1 Extension of expert defined rules
- 5.4.2 The modified least squares algorithm
- 5.4.3 Updating a priori given rules
- 5.5 Unknown rule structure
- 5.5.1 Elements to be determined
- 5.5.2 The b-cut algorithm
- 5.5.3 Least squares rule estimation
- 5.5.4 Other methods
- 5.6 Deriving rule systems from fuzzy data
- 5.6.1 Fuzzy premises
- 5.6.2 Fuzzy responses
- 5.6.3 Fuzzy premises and fuzzy responses
- 5.6.4 Additional remarks
- 5.7 Rule verification
- 5.8 Removing unnecessary rules
- 6 Fuzzy rule-based modeling versus fuzzy control
- 6.1 Principles of fuzzy control
- 6.2 Examples of fuzzy control
- 6.2.1 Inverted pendulum
- 6.2.2 Automatic train operation system
- 6.3 Fuzzy control and fuzzy rule-based modeling
- 6.3.1 Closed loop versus open loop
- 6.3.2 Fuzzy set membership function assessment
- 6.3.3 Derivation of rules
- 6.3.4 Rule combination and defuzzification
- 6.3.5 Further remarks
- 7 Rule systems with discrete responses
- 7.1 Combination of discrete consequence type rules
- 7.1.1 Minimum combinations
- 7.1.2 Maximum combinations
- 7.1.3 Additive combinations
- 7.2 Rule assessment
- 7.3 Application to weather classification
- 7.3.1 Classifications approaches
- 7.3.2 Numerical example
- 7.3.3 Use for precipitation modeling
- 8 Application to time series
- 8.1 Rule assessment
- 8.1.1 Adaptive rule assessment
- 8.1.2 Rule assessment from trajectories
- 8.2 Example: Water demand forecasting
- 8.2.1 The model
- 8.2.2 Comparison to other models
- 8.2.3 Application to forecasting
- 8.3 Example: Daily mean temperature
- 9 Application to dynamical physical systems
- 9.1 Application to soil water movement
- 9.1.1 Application
- 9.1.2 Extension to two and three dimensions
- 9.1.3 Comparison to numerical solutions
- 9.1.4 Discussion
- 10 Other applications
- 10.1 Application to medical diagnosis
- 10.1.1 Construction of the questionnaire
- 10.1.2 Information acquisition
- 10.1.3 Target definition
- 10.1.4 Selection of method evaluation and internal consistency criteria
- 10.1.5 Membership function construction and discriminant parameter selection
- 10.1.6 Fuzzy rule construction and prior sensitivity analysis
- 10.1.7 Application of the fuzzy rule to the validation set
- 10.1.8 Discussion and conclusions
- 10.2 Sustainable reservoir operation
- 10.2.1 Sustainable development
- 10.2.2 Fuzzy reservoir model and results
- 10.2.3 Discussion and conclusions
- References
- Appendix A: Proofs of selected propositions
- Index
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