| Description |
1 online resource |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
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 Engines -- 1.1.12 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 Distributions -- 2.1.2 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 Variable. |
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3.2.2 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 Uncertainty -- 4.5.2 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 -- 5.5.2 Reducing the Time Required to Generate the Training Data -- 5.6 Summary -- References -- II CONTROL OF INFORMATION-POOR SYSTEMS -- 6 Fuzzy Decision-Making -- 6.1 Risk Assessment in Information-Poor Systems -- 6.2 Fuzzy Optimization in Information-Poor Systems -- 6.2.1 Fuzzy Goals and Fuzzy Constraints -- 6.2.2 Fuzzy Aggregation Operators -- 6.2.3 Fuzzy Ranking -- 6.3 Multi-Stage Decision-Making -- 6.3.1 Fuzzy Dynamic Programming -- 6.3.2 Branch and Bound -- 6.3.3 Genetic Algorithms -- 6.4 Fuzzy Decision-Making Based on Intuitionistic Fuzzy Sets -- 6.4.1 Definition of an Intuitionistic Fuzzy Set. |
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6.4.2 Multi-Attribute Decision-Making Using Intuitionistic Fuzzy Numbers -- 6.5 Summary -- References -- 7 Predictive Control in Uncertain Systems -- 7.1 Model-Based Predictive Control -- 7.2 Fuzzy Approaches to Model-Based Control of Uncertain Systems -- 7.2.1 Inverse Control of Fuzzy Interval Systems -- 7.2.2 Fuzzy Model-Based Predictive Control -- 7.3 Practical Issues Associated with Multi-Step Fuzzy Decision-Making -- 7.3.1 Limiting the Accumulation of Uncertainty -- 7.3.2 Avoiding Excessive Computational Demands When Using Enumerative Search Optimization -- 7.3.3 Avoiding Excessive Computational Demands When Using Evolutionary Algorithms -- 7.3.4 Handling Infeasibility -- 7.3.5 Choosing the Weighting in Multi-Criteria Cost Functions -- 7.3.6 Dealing with Hard Constraints -- 7.4 A Simplified Approach to Fuzzy FRM-Based Predictive Control -- 7.4.1 The Fuzzy Decision-Maker -- 7.4.2 Conditional Defuzzification -- 7.5 FMPC of an Uncertain Dynamic System Based on a Generic Fuzzy FRM -- 7.6 Summary -- References -- 8 Incorporating Fuzzy Inputs -- 8.1 Fuzzy Setpoints and Fuzzy Measurements -- 8.1.1 Fuzzy Setpoints -- 8.1.2 Fuzzy Measurements -- 8.2 Fuzzy Measures of the Tracking Error and its Derivative -- 8.3 Inference with Fuzzy Inputs -- 8.4 Fuzzy Output Neural Networks -- 8.5 Modelling Input Uncertainty Using a Fuzzy FRM -- 8.6 Summary -- References -- 9 Disturbance Rejection in Information-Poor Systems -- 9.1 Rejecting Unmeasured Disturbances in Uncertain Systems -- 9.1.1 Robust Fuzzy Control -- 9.1.2 Feedback Linearization Using a Fuzzy Disturbance Observer -- 9.1.3 Fuzzy Model-Based Internal Model Control -- 9.2 Fuzzy IMC Based on a Fuzzy Output FRM -- 9.3 Rejecting Measured Disturbances in Non-Linear Uncertain Systems -- 9.4 Fuzzy MPC with Feedforward -- 9.5 Summary -- References -- III ONLINE LEARNING IN INFORMATION-POOR SYSTEMS. |
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10 Online Model Identification in Information-Poor Environments -- 10.1 Online Fuzzy Identification Schemes -- 10.1.1 Recursive Fuzzy Least-Squares -- 10.1.2 Recursive Forms of the RSK Algorithm -- 10.2 Effect of Poor-Quality and Incomplete Training Data -- 10.3 Ways of Reducing the Computational Demands -- 10.3.1 Evolving Fuzzy Models -- 10.3.2 Hierarchical Fuzzy Models -- 10.4 Summary -- References -- 11 Adaptive Model-Based Control of Information-Poor Systems -- 11.1 Robust Adaptive Fuzzy Control -- 11.2 Adaptive Fuzzy FRM-Based Predictive Control -- 11.3 Commissioning the Controller -- 11.3.1 Methods of Incorporating Prior Knowledge -- 11.3.2 Initialization Using a Generic Fuzzy FRM -- 11.4 Generating an Optimal Control Signal Using a Partially Trained Model -- 11.4.1 Taking the Amount of Training into Account -- 11.4.2 Incorporating a Secondary Controller -- 11.4.3 Combining the Fuzzy Predictions Generated by More than One Model -- 11.5 Dealing with the Effects of Disturbances -- 11.5.1 Adaptive Feedforward Control Based on an Inaccurate Disturbance Measurement -- 11.6 Summary -- References -- 12 Adaptive Model-Free Control of Information-Poor Systems -- 12.1 Introduction to Model-Free Adaptive Control of Non-Linear Systems -- 12.2 Fuzzy FRM-Based Direct Adaptive Control -- 12.3 Behaviour in the Presence of a Noisy Measurement of the Plant Output -- 12.4 Behaviour in the Presence of an Unmeasured Disturbance -- 12.5 Accounting for Uncertainty Arising from a Measured Disturbance -- 12.6 Summary -- References -- 13 Fault Diagnosis in Information-Poor Systems -- 13.1 Introduction to Fault Detection and Isolation in Non-Linear Uncertain Systems -- 13.1.1 Model-Based Methods for Non-Linear Systems -- 13.1.2 Ways of Accounting for Uncertainty -- 13.2 A Fuzzy FRM-Based Fault Diagnosis Scheme -- 13.2.1 Measuring the Similarity of FRMs. |
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13.2.2 Accumulating Evidence of Fault-Free or Faulty Operation -- 13.2.3 Generating Robust Generic Models of Faulty Operation -- 13.2.4 Multi-Step Fault Diagnosis -- 13.3 Summary -- References -- IV SOME EXAMPLE APPLICATIONS -- 14 Control of Thermal Comfort -- 14.1 Main Sources of Uncertainty and Practical Considerations -- 14.2 Review of Approaches Suggested for Dealing with the Uncertainty -- 14.3 Design of the Fuzzy FRM-Based Control System -- 14.3.1 The Fuzzy FRM -- 14.3.2 The Fuzzy Cost Functions -- 14.3.3 The Fuzzy Goals -- 14.3.4 The Fuzzy Decision-Maker -- 14.3.5 The Conditional Defuzzifier -- 14.4 Performance of the Thermal Comfort Controller -- 14.5 Concluding Remarks -- References -- 15 Identification of Faults in Air-Conditioning Systems -- 15.1 Main Sources of Uncertainty and Practical Considerations -- 15.2 Design of a Fuzzy FRM-Based Monitoring System for a Cooling Coil Subsystem -- 15.3 Diagnosis of Known Faults in a Simulated Cooling Coil Subsystem -- 15.3.1 Fault-Free Operation -- 15.3.2 Leaky Valve -- 15.3.3 Fouled Coil -- 15.3.4 Valve Stuck in the Fully Closed Position -- 15.3.5 Valve Stuck in the Midway Position -- 15.3.6 Valve Stuck in the Fully Open Position -- 15.4 Commissioning of Air-Handling Units -- 15.5 Concluding Remarks -- References -- 16 Control of Heat Exchangers -- 16.1 Main Sources of Uncertainty and Practical Considerations -- 16.2 Design of a Fuzzy FRM-Based Predictive Controller -- 16.3 Design of a Fuzzy FRM-Based Internal Model Control Scheme -- 16.4 Concluding Remarks -- References -- 17 Measurement of Spatially Distributed Quantities -- 17.1 Review of Approaches Suggested for Dealing with Sensor Bias -- 17.2 An Example Application -- 17.2.1 Air Temperature Estimation Using a Single-Point Sensor with Bias Correction -- 17.2.2 Air Temperature Estimation Based on Mass and Energy Balances. |
| Summary |
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 into account. This book describes an approach to the monitoring and control of information-poor systems that is based on fuzzy relational models which generate fuzzy outputs. The first part of Monitoring and Control of Information-Poor Systems aims to clarify why design decisions must take account of the uncertainty associated with optimal choices, and to explain how a fuzzy relational model can be used to generate a fuzzy output, which reflects the uncertainties associated with its predictions. Part two gives a brief introduction to fuzzy decision-making and shows how it can be used to design a predictive control scheme that is suitable for controlling information-poor systems using inaccurate measurements. Part three describes different ways in which fuzzy relational models can be generated online and explains the practical issues associated with their identification and application. The final part of the book provides examples of the use of the previously described techniques in real applications. Key features: Describes techniques applicable to a wide range of engineering, environmental, medical, financial and economic applications Uses simple examples to help explain the basic techniques for dealing with uncertainty Describes a novel design approach based on the use of fuzzy relational models Considers practical issues associated with applying the techniques to real systems Monitoring and Control of Information-Poor Systems forms an invaluable resource for a wide range of graduate students, and is also a comprehensive reference for. |
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Researchers and practitioners working on problems involving mathematical modelling and control. |
| Subject |
Engineering -- Statistical methods.
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Uncertainty -- Mathematical models.
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Fuzzy systems.
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Control theory.
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Ingénierie -- Méthodes statistiques. |
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Systèmes flous. |
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Théorie de la commande. |
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Control theory |
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Engineering -- Statistical methods |
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Fuzzy systems |
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Uncertainty -- Mathematical models |
| Other Form: |
Print version: 9786613618177 (DLC) 2011046728 |
| ISBN |
9780470688694 (Cloth) |
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0470688696 (Cloth) |
| Standard No. |
9786613618177 |
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