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Produktbild: Monitoring and Control of Information-Poor Systems

Monitoring and Control of Information-Poor Systems An Approach Based on Fuzzy Relational Models

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

16.04.2012

Verlag

John Wiley & Sons

Seitenzahl

336

Maße (L/B/H)

24,9/17/2 cm

Gewicht

635 g

Auflage

2. Auflage

Sprache

Englisch

ISBN

978-0-470-68869-4

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

16.04.2012

Verlag

John Wiley & Sons

Seitenzahl

336

Maße (L/B/H)

24,9/17/2 cm

Gewicht

635 g

Auflage

2. Auflage

Sprache

Englisch

ISBN

978-0-470-68869-4

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Monitoring and Control of Information-Poor Systems
  • Preface xi
     
    About the Author xv
     
    Acknowledgements xvii
     
    I ANALYSING THE BEHAVIOUR OF INFORMATION-POOR SYSTEMS
     
    1 Characteristics of Information-Poor Systems 3
     
    1.1 Introduction to Information-Poor Systems 3
     
    1.1.1 Blast Furnaces 3
     
    1.1.2 Container Cranes 3
     
    1.1.3 Cooperative Control Systems 4
     
    1.1.4 Distillation Columns 4
     
    1.1.5 Drug Administration 4
     
    1.1.6 Electrical Power Generation and Distribution 4
     
    1.1.7 Environmental Risk Assessment Systems 4
     
    1.1.8 Financial Investment and Portfolio Selection 5
     
    1.1.9 Health Care Systems 5
     
    1.1.10 Indoor Climate Control 5
     
    1.1.11 NOx Emissions from Gas Turbines and Internal Combustion Engines 6
     
    1.1.12 Penicillin Production Plant 6
     
    1.1.13 Polymerization Reactors 6
     
    1.1.14 Rotary Kilns 6
     
    1.1.15 Solar Power Plant 7
     
    1.1.16 Wastewater Treatment Plant 7
     
    1.1.17 Wood Pulp Production Plant 7
     
    1.2 Main Causes of Uncertainty 7
     
    1.2.1 Sources of Modelling Errors 8
     
    1.2.2 Sources of Measurement Errors 8
     
    1.2.3 Reasons for Poorly Defined Objectives and Constraints 9
     
    1.3 Design in the Face of Uncertainty 9
     
    References 9
     
    2 Describing and Propagating Uncertainty 13
     
    2.1 Methods of Describing Uncertainty 13
     
    2.1.1 Uncertainty Intervals and Probability Distributions 13
     
    2.1.2 Fuzzy Sets and Fuzzy Numbers 14
     
    2.2 Methods of Propagating Uncertainty 15
     
    2.2.1 Interval Arithmetic 15
     
    2.2.2 Statistical Methods 16
     
    2.2.3 Monte Carlo Methods 16
     
    2.2.4 Fuzzy Arithmetic 17
     
    2.3 Fuzzy Arithmetic Using ±-Cut Sets and Interval Arithmetic 18
     
    2.4 Fuzzy Arithmetic Based on the Extension Principle 21
     
    2.5 Representing and Propagating Uncertainty Using Pseudo-Triangular Membership Functions 24
     
    2.6 Summary 27
     
    References 27
     
    3 Accounting for Measurement Uncertainty 29
     
    3.1 Measurement Errors 29
     
    3.2 Introduction to Fuzzy Random Variables 29
     
    3.2.1 Definition of a Fuzzy Random Variable 30
     
    3.2.2 Generating Fuzzy Random Variables from a Knowledge of the Random and Systematic Errors 30
     
    3.3 A Hybrid Approach to the Propagation of Uncertainty 32
     
    3.4 Fuzzy Sensor Fusion Based on the Extension Principle 34
     
    3.5 Fuzzy Sensors 38
     
    3.6 Summary 39
     
    References 39
     
    4 Accounting for Modelling Errors in Fuzzy Models 41
     
    4.1 An Introduction to Rule-Based Models 41
     
    4.2 Linguistic Fuzzy Models 41
     
    4.2.1 Fuzzy Rules 41
     
    4.2.2 Fuzzy Inferencing 42
     
    4.2.3 Compositional Rules of Inference 43
     
    4.3 Functional Fuzzy Models 47
     
    4.4 Fuzzy Neural Networks 48
     
    4.5 Methods of Generating Fuzzy Models 50
     
    4.5.1 Modifying Expert Rules to Take Account of Uncertainty 50
     
    4.5.2 Identifying Fuzzy Rules from Data 56
     
    4.6 Defuzzification 58
     
    4.7 Summary 60
     
    References 60
     
    5 Fuzzy Relational Models 63
     
    5.1 Introduction to Fuzzy Relations and Fuzzy Relational Models 63
     
    5.2 Fuzzy FRMs 65
     
    5.3 Methods of Estimating Rule Confidences from Data 67
     
    5.4 Estimating Probability Density Functions from Data 70
     
    5.4.1 Probabilistic Interpretation of RSK Fuzzy Identification 71
     
    5.4.2 Effect of Structural Errors on the Output of a Fuzzy FRM 78
     
    5.4.3 Estimation Based on Limited Amounts of Training Data 83
     
    5.5 Generic Fuzzy Models 86
     
    5.5.1 Identification of Generic