Monitoring and control of information-poor systems
The monitoring and control of a system whose behaviour is highly uncertain is an important and challenging practical problem. Methods of solution based on fuzzy techniques have generated considerable interest, but very little of the existing literature considers explicit ways of taking uncertainties...
| Autor principal: | |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Chichester, West Sussex, U.K. ; Hoboken, NJ :
Wiley
2012.
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| Edición: | 2nd ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628157106719 |
Tabla de Contenidos:
- MONITORING AND CONTROL OF INFORMATION-POOR SYSTEMS; Contents; Preface; About the Author; Acknowledgements; I ANALYSING THE BEHAVIOUR OF INFORMATION-POOR SYSTEMS; 1 Characteristics of Information-Poor Systems; 1.1 Introduction to Information-Poor Systems; 1.1.1 Blast Furnaces; 1.1.2 Container Cranes; 1.1.3 Cooperative Control Systems; 1.1.4 Distillation Columns; 1.1.5 Drug Administration; 1.1.6 Electrical Power Generation and Distribution; 1.1.7 Environmental Risk Assessment Systems; 1.1.8 Financial Investment and Portfolio Selection; 1.1.9 Health Care Systems; 1.1.10 Indoor Climate Control
- 1.1.11 NOx Emissions from Gas Turbines and Internal Combustion Engines1.1.12 Penicillin Production Plant; 1.1.13 Polymerization Reactors; 1.1.14 Rotary Kilns; 1.1.15 Solar Power Plant; 1.1.16 Wastewater Treatment Plant; 1.1.17 Wood Pulp Production Plant; 1.2 Main Causes of Uncertainty; 1.2.1 Sources of Modelling Errors; 1.2.2 Sources of Measurement Errors; 1.2.3 Reasons for Poorly Defined Objectives and Constraints; 1.3 Design in the Face of Uncertainty; References; 2 Describing and Propagating Uncertainty; 2.1 Methods of Describing Uncertainty
- 2.1.1 Uncertainty Intervals and Probability Distributions2.1.2 Fuzzy Sets and Fuzzy Numbers; 2.2 Methods of Propagating Uncertainty; 2.2.1 Interval Arithmetic; 2.2.2 Statistical Methods; 2.2.3 Monte Carlo Methods; 2.2.4 Fuzzy Arithmetic; 2.3 Fuzzy Arithmetic Using a-Cut Sets and Interval Arithmetic; 2.4 Fuzzy Arithmetic Based on the Extension Principle; 2.5 Representing and Propagating Uncertainty Using Pseudo-Triangular Membership Functions; 2.6 Summary; References; 3 Accounting for Measurement Uncertainty; 3.1 Measurement Errors; 3.2 Introduction to Fuzzy Random Variables
- 3.2.1 Definition of a Fuzzy Random Variable3.2.2 Generating Fuzzy Random Variables from a Knowledge of the Random and Systematic Errors; 3.3 A Hybrid Approach to the Propagation of Uncertainty; 3.4 Fuzzy Sensor Fusion Based on the Extension Principle; 3.5 Fuzzy Sensors; 3.6 Summary; References; 4 Accounting for Modelling Errors in Fuzzy Models; 4.1 An Introduction to Rule-Based Models; 4.2 Linguistic Fuzzy Models; 4.2.1 Fuzzy Rules; 4.2.2 Fuzzy Inferencing; 4.2.3 Compositional Rules of Inference; 4.3 Functional Fuzzy Models; 4.4 Fuzzy Neural Networks; 4.5 Methods of Generating Fuzzy Models
- 4.5.1 Modifying Expert Rules to Take Account of Uncertainty4.5.2 Identifying Fuzzy Rules from Data; 4.6 Defuzzification; 4.7 Summary; References; 5 Fuzzy Relational Models; 5.1 Introduction to Fuzzy Relations and Fuzzy Relational Models; 5.2 Fuzzy FRMs; 5.3 Methods of Estimating Rule Confidences from Data; 5.4 Estimating Probability Density Functions from Data; 5.4.1 Probabilistic Interpretation of RSK Fuzzy Identification; 5.4.2 Effect of Structural Errors on the Output of a Fuzzy FRM; 5.4.3 Estimation Based on Limited Amounts of Training Data; 5.5 Generic Fuzzy Models
- 5.5.1 Identification of Generic Fuzzy Models
