{"product_id":"monitoring-and-control-of-informationpoor-systems-9780470688694","title":"Monitoring and Control of InformationPoor Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eThe 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cb\u003ePreface xi\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eAbout the Author xv\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAcknowledgements xvii\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eI ANALYSING THE BEHAVIOUR OF INFORMATION-POOR SYSTEMS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Characteristics of Information-Poor Systems 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction to Information-Poor Systems 3\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.1 Blast Furnaces\u003c\/i\u003e 3\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.2 Container Cranes\u003c\/i\u003e 3\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.3 Cooperative Control Systems\u003c\/i\u003e 4\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.4 Distillation Columns\u003c\/i\u003e 4\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.5 Drug Administration\u003c\/i\u003e 4\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.6 Electrical Power Generation and Distribution\u003c\/i\u003e 4\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.7 Environmental Risk Assessment Systems\u003c\/i\u003e 4\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.8 Financial Investment and Portfolio Selection\u003c\/i\u003e 5\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.9 Health Care Systems\u003c\/i\u003e 5\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.10 Indoor Climate Control\u003c\/i\u003e 5\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.11 NOx Emissions from Gas Turbines and Internal Combustion Engines\u003c\/i\u003e 6\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.12 Penicillin Production Plant\u003c\/i\u003e 6\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.13 Polymerization Reactors\u003c\/i\u003e 6\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.14 Rotary Kilns\u003c\/i\u003e 6\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.15 Solar Power Plant\u003c\/i\u003e 7\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.16 Wastewater Treatment Plant\u003c\/i\u003e 7\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.1.17 Wood Pulp Production Plant\u003c\/i\u003e 7\u003c\/p\u003e \u003cp\u003e1.2 Main Causes of Uncertainty 7\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.2.1 Sources of Modelling Errors\u003c\/i\u003e 8\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.2.2 Sources of Measurement Errors\u003c\/i\u003e 8\u003c\/p\u003e \u003cp\u003e\u003ci\u003e1.2.3 Reasons for Poorly Defined Objectives and Constraints\u003c\/i\u003e 9\u003c\/p\u003e \u003cp\u003e1.3 Design in the Face of Uncertainty 9\u003c\/p\u003e \u003cp\u003eReferences 9\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Describing and Propagating Uncertainty 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Methods of Describing Uncertainty 13\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.1.1 Uncertainty Intervals and Probability Distributions\u003c\/i\u003e 13\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.1.2 Fuzzy Sets and Fuzzy Numbers\u003c\/i\u003e 14\u003c\/p\u003e \u003cp\u003e2.2 Methods of Propagating Uncertainty 15\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.1 Interval Arithmetic\u003c\/i\u003e 15\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.2 Statistical Methods\u003c\/i\u003e 16\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.3 Monte Carlo Methods\u003c\/i\u003e 16\u003c\/p\u003e \u003cp\u003e\u003ci\u003e2.2.4 Fuzzy Arithmetic\u003c\/i\u003e 17\u003c\/p\u003e \u003cp\u003e2.3 Fuzzy Arithmetic Using \u003ci\u003eα\u003c\/i\u003e-Cut Sets and Interval Arithmetic 18\u003c\/p\u003e \u003cp\u003e2.4 Fuzzy Arithmetic Based on the Extension Principle 21\u003c\/p\u003e \u003cp\u003e2.5 Representing and Propagating Uncertainty Using Pseudo-Triangular Membership Functions 24\u003c\/p\u003e \u003cp\u003e2.6 Summary 27\u003c\/p\u003e \u003cp\u003eReferences 27\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Accounting for Measurement Uncertainty 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Measurement Errors 29\u003c\/p\u003e \u003cp\u003e3.2 Introduction to Fuzzy Random Variables 29\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.1 Definition of a Fuzzy Random Variable\u003c\/i\u003e 30\u003c\/p\u003e \u003cp\u003e\u003ci\u003e3.2.2 Generating Fuzzy Random Variables from a Knowledge of the Random and Systematic Errors\u003c\/i\u003e 30\u003c\/p\u003e \u003cp\u003e3.3 A Hybrid Approach to the Propagation of Uncertainty 32\u003c\/p\u003e \u003cp\u003e3.4 Fuzzy Sensor Fusion Based on the Extension Principle 34\u003c\/p\u003e \u003cp\u003e3.5 Fuzzy Sensors 38\u003c\/p\u003e \u003cp\u003e3.6 Summary 39\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Accounting for Modelling Errors in Fuzzy Models 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 An Introduction to Rule-Based Models 41\u003c\/p\u003e \u003cp\u003e4.2 Linguistic Fuzzy Models 41\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.2.1 Fuzzy Rules\u003c\/i\u003e 41\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.2.2 Fuzzy Inferencing\u003c\/i\u003e 42\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.2.3 Compositional Rules of Inference\u003c\/i\u003e 43\u003c\/p\u003e \u003cp\u003e4.3 Functional Fuzzy Models 47\u003c\/p\u003e \u003cp\u003e4.4 Fuzzy Neural Networks 48\u003c\/p\u003e \u003cp\u003e4.5 Methods of Generating Fuzzy Models 50\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.1 Modifying Expert Rules to Take Account of Uncertainty\u003c\/i\u003e 50\u003c\/p\u003e \u003cp\u003e\u003ci\u003e4.5.2 Identifying Fuzzy Rules from Data\u003c\/i\u003e 56\u003c\/p\u003e \u003cp\u003e4.6 Defuzzification 58\u003c\/p\u003e \u003cp\u003e4.7 Summary 60\u003c\/p\u003e \u003cp\u003eReferences 60\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Fuzzy Relational Models 63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction to Fuzzy Relations and Fuzzy Relational Models 63\u003c\/p\u003e \u003cp\u003e5.2 Fuzzy FRMs 65\u003c\/p\u003e \u003cp\u003e5.3 Methods of Estimating Rule Confidences from Data 67\u003c\/p\u003e \u003cp\u003e5.4 Estimating Probability Density Functions from Data 70\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.4.1 Probabilistic Interpretation of RSK Fuzzy Identification\u003c\/i\u003e 71\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.4.2 Effect of Structural Errors on the Output of a Fuzzy FRM\u003c\/i\u003e 78\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.4.3 Estimation Based on Limited Amounts of Training Data\u003c\/i\u003e 83\u003c\/p\u003e \u003cp\u003e5.5 Generic Fuzzy Models 86\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.5.1 Identification of Generic Fuzzy Models\u003c\/i\u003e 87\u003c\/p\u003e \u003cp\u003e\u003ci\u003e5.5.2 Reducing the Time Required to Generate the Training Data\u003c\/i\u003e 91\u003c\/p\u003e \u003cp\u003e5.6 Summary 92\u003c\/p\u003e \u003cp\u003eReferences 92\u003c\/p\u003e \u003cp\u003e\u003cb\u003eII CONTROL OF INFORMATION-POOR SYSTEMS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Fuzzy Decision-Making 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Risk Assessment in Information-Poor Systems 97\u003c\/p\u003e \u003cp\u003e6.2 Fuzzy Optimization in Information-Poor Systems 99\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.2.1 Fuzzy Goals and Fuzzy Constraints\u003c\/i\u003e 99\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.2.2 Fuzzy Aggregation Operators\u003c\/i\u003e 99\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.2.3 Fuzzy Ranking\u003c\/i\u003e 100\u003c\/p\u003e \u003cp\u003e6.3 Multi-Stage Decision-Making 101\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.3.1 Fuzzy Dynamic Programming\u003c\/i\u003e 102\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.3.2 Branch and Bound\u003c\/i\u003e 103\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.3.3 Genetic Algorithms\u003c\/i\u003e 106\u003c\/p\u003e \u003cp\u003e6.4 Fuzzy Decision-Making Based on Intuitionistic Fuzzy Sets 106\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.4.1 Definition of an Intuitionistic Fuzzy Set\u003c\/i\u003e 106\u003c\/p\u003e \u003cp\u003e\u003ci\u003e6.4.2 Multi-Attribute Decision-Making Using Intuitionistic Fuzzy Numbers\u003c\/i\u003e 107\u003c\/p\u003e \u003cp\u003e6.5 Summary 108\u003c\/p\u003e \u003cp\u003eReferences 108\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Predictive Control in Uncertain Systems 111\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Model-Based Predictive Control 111\u003c\/p\u003e \u003cp\u003e7.2 Fuzzy Approaches to Model-Based Control of Uncertain Systems 112\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.2.1 Inverse Control of Fuzzy Interval Systems\u003c\/i\u003e 112\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.2.2 Fuzzy Model-Based Predictive Control\u003c\/i\u003e 114\u003c\/p\u003e \u003cp\u003e7.3 Practical Issues Associated with Multi-Step Fuzzy Decision-Making 115\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.1 Limiting the Accumulation of Uncertainty\u003c\/i\u003e 115\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.2 Avoiding Excessive Computational Demands When Using Enumerative Search Optimization\u003c\/i\u003e 115\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.3 Avoiding Excessive Computational Demands When Using Evolutionary Algorithms\u003c\/i\u003e 116\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.4 Handling Infeasibility\u003c\/i\u003e 117\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.5 Choosing the Weighting in Multi-Criteria Cost Functions\u003c\/i\u003e 117\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.3.6 Dealing with Hard Constraints\u003c\/i\u003e 118\u003c\/p\u003e \u003cp\u003e7.4 A Simplified Approach to Fuzzy FRM-Based Predictive Control 118\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.1 The Fuzzy Decision-Maker\u003c\/i\u003e 119\u003c\/p\u003e \u003cp\u003e\u003ci\u003e7.4.2 Conditional Defuzzification\u003c\/i\u003e 120\u003c\/p\u003e \u003cp\u003e7.5 FMPC of an Uncertain Dynamic System Based on a Generic Fuzzy FRM 122\u003c\/p\u003e \u003cp\u003e7.6 Summary 127\u003c\/p\u003e \u003cp\u003eReferences 128\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Incorporating Fuzzy Inputs 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Fuzzy Setpoints and Fuzzy Measurements 129\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.1.1 Fuzzy Setpoints\u003c\/i\u003e 129\u003c\/p\u003e \u003cp\u003e\u003ci\u003e8.1.2 Fuzzy Measurements\u003c\/i\u003e 129\u003c\/p\u003e \u003cp\u003e8.2 Fuzzy Measures of the Tracking Error and its Derivative 131\u003c\/p\u003e \u003cp\u003e8.3 Inference with Fuzzy Inputs 136\u003c\/p\u003e \u003cp\u003e8.4 Fuzzy Output Neural Networks 138\u003c\/p\u003e \u003cp\u003e8.5 Modelling Input Uncertainty Using a Fuzzy FRM 140\u003c\/p\u003e \u003cp\u003e8.6 Summary 151\u003c\/p\u003e \u003cp\u003eReferences 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Disturbance Rejection in Information-Poor Systems 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Rejecting Unmeasured Disturbances in Uncertain Systems 154\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.1.1 Robust Fuzzy Control\u003c\/i\u003e 154\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.1.2 Feedback Linearization Using a Fuzzy Disturbance Observer\u003c\/i\u003e 155\u003c\/p\u003e \u003cp\u003e\u003ci\u003e9.1.3 Fuzzy Model-Based Internal Model Control\u003c\/i\u003e 155\u003c\/p\u003e \u003cp\u003e9.2 Fuzzy IMC Based on a Fuzzy Output FRM 157\u003c\/p\u003e \u003cp\u003e9.3 Rejecting Measured Disturbances in Non-Linear Uncertain Systems 161\u003c\/p\u003e \u003cp\u003e9.4 Fuzzy MPC with Feedforward 162\u003c\/p\u003e \u003cp\u003e9.5 Summary 166\u003c\/p\u003e \u003cp\u003eReferences 166\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIII ONLINE LEARNING IN INFORMATION-POOR SYSTEMS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Online Model Identification in Information-Poor Environments 171\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Online Fuzzy Identification Schemes 171\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.1.1 Recursive Fuzzy Least-Squares\u003c\/i\u003e 171\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.1.2 Recursive Forms of the RSK Algorithm\u003c\/i\u003e 172\u003c\/p\u003e \u003cp\u003e10.2 Effect of Poor-Quality and Incomplete Training Data 176\u003c\/p\u003e \u003cp\u003e10.3 Ways of Reducing the Computational Demands 177\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.3.1 Evolving Fuzzy Models\u003c\/i\u003e 177\u003c\/p\u003e \u003cp\u003e\u003ci\u003e10.3.2 Hierarchical Fuzzy Models\u003c\/i\u003e 181\u003c\/p\u003e \u003cp\u003e10.4 Summary 185\u003c\/p\u003e \u003cp\u003eReferences 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Adaptive Model-Based Control of Information-Poor Systems 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Robust Adaptive Fuzzy Control 187\u003c\/p\u003e \u003cp\u003e11.2 Adaptive Fuzzy FRM-Based Predictive Control 188\u003c\/p\u003e \u003cp\u003e11.3 Commissioning the Controller 189\u003c\/p\u003e \u003cp\u003e\u003ci\u003e11.3.1 Methods of Incorporating Prior Knowledge\u003c\/i\u003e 189\u003c\/p\u003e \u003cp\u003e\u003ci\u003e11.3.2 Initialization Using a Generic Fuzzy FRM\u003c\/i\u003e 189\u003c\/p\u003e \u003cp\u003e11.4 Generating an Optimal Control Signal Using a Partially Trained Model 192\u003c\/p\u003e \u003cp\u003e\u003ci\u003e11.4.1 Taking the Amount of Training into Account\u003c\/i\u003e 192\u003c\/p\u003e \u003cp\u003e\u003ci\u003e11.4.2 Incorporating a Secondary Controller\u003c\/i\u003e 194\u003c\/p\u003e \u003cp\u003e\u003ci\u003e11.4.3 Combining the Fuzzy Predictions Generated by More than One Model\u003c\/i\u003e 201\u003c\/p\u003e \u003cp\u003e11.5 Dealing with the Effects of Disturbances 202\u003c\/p\u003e \u003cp\u003e\u003ci\u003e11.5.1 Adaptive Feedforward Control Based on an Inaccurate Disturbance Measurement\u003c\/i\u003e 203\u003c\/p\u003e \u003cp\u003e11.6 Summary 209\u003c\/p\u003e \u003cp\u003eReferences 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Adaptive Model-Free Control of Information-Poor Systems 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction to Model-Free Adaptive Control of Non-Linear Systems 211\u003c\/p\u003e \u003cp\u003e12.2 Fuzzy FRM-Based Direct Adaptive Control 211\u003c\/p\u003e \u003cp\u003e12.3 Behaviour in the Presence of a Noisy Measurement of the Plant Output 213\u003c\/p\u003e \u003cp\u003e12.4 Behaviour in the Presence of an Unmeasured Disturbance 218\u003c\/p\u003e \u003cp\u003e12.5 Accounting for Uncertainty Arising from a Measured Disturbance 222\u003c\/p\u003e \u003cp\u003e12.6 Summary 227\u003c\/p\u003e \u003cp\u003eReferences 227\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Fault Diagnosis in Information-Poor Systems 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction to Fault Detection and Isolation in Non-Linear Uncertain Systems 229\u003c\/p\u003e \u003cp\u003e\u003ci\u003e13.1.1 Model-Based Methods for Non-Linear Systems\u003c\/i\u003e 230\u003c\/p\u003e \u003cp\u003e\u003ci\u003e13.1.2 Ways of Accounting for Uncertainty\u003c\/i\u003e 232\u003c\/p\u003e \u003cp\u003e13.2 A Fuzzy FRM-Based Fault Diagnosis Scheme 233\u003c\/p\u003e \u003cp\u003e\u003ci\u003e13.2.1 Measuring the Similarity of FRMs\u003c\/i\u003e 234\u003c\/p\u003e \u003cp\u003e\u003ci\u003e13.2.2 Accumulating Evidence of Fault-Free or Faulty Operation\u003c\/i\u003e 236\u003c\/p\u003e \u003cp\u003e\u003ci\u003e13.2.3 Generating Robust Generic Models of Faulty Operation\u003c\/i\u003e 239\u003c\/p\u003e \u003cp\u003e\u003ci\u003e13.2.4 Multi-Step Fault Diagnosis\u003c\/i\u003e 239\u003c\/p\u003e \u003cp\u003e13.3 Summary 242\u003c\/p\u003e \u003cp\u003eReferences 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIV SOME EXAMPLE APPLICATIONS\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Control of Thermal Comfort 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Main Sources of Uncertainty and Practical Considerations 248\u003c\/p\u003e \u003cp\u003e14.2 Review of Approaches Suggested for Dealing with the Uncertainty 249\u003c\/p\u003e \u003cp\u003e14.3 Design of the Fuzzy FRM-Based Control System 249\u003c\/p\u003e \u003cp\u003e\u003ci\u003e14.3.1 The Fuzzy FRM\u003c\/i\u003e 250\u003c\/p\u003e \u003cp\u003e\u003ci\u003e14.3.2 The Fuzzy Cost Functions\u003c\/i\u003e 252\u003c\/p\u003e \u003cp\u003e\u003ci\u003e14.3.3 The Fuzzy Goals\u003c\/i\u003e 252\u003c\/p\u003e \u003cp\u003e\u003ci\u003e14.3.4 The Fuzzy Decision-Maker\u003c\/i\u003e 254\u003c\/p\u003e \u003cp\u003e\u003ci\u003e14.3.5 The Conditional Defuzzifier\u003c\/i\u003e 254\u003c\/p\u003e \u003cp\u003e14.4 Performance of the Thermal Comfort Controller 254\u003c\/p\u003e \u003cp\u003e14.5 Concluding Remarks 258\u003c\/p\u003e \u003cp\u003eReferences 259\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Identification of Faults in Air-Conditioning Systems 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Main Sources of Uncertainty and Practical Considerations 261\u003c\/p\u003e \u003cp\u003e15.2 Design of a Fuzzy FRM-Based Monitoring System for a Cooling Coil Subsystem 263\u003c\/p\u003e \u003cp\u003e15.3 Diagnosis of Known Faults in a Simulated Cooling Coil Subsystem 264\u003c\/p\u003e \u003cp\u003e\u003ci\u003e15.3.1 Fault-Free Operation\u003c\/i\u003e 264\u003c\/p\u003e \u003cp\u003e\u003ci\u003e15.3.2 Leaky Valve\u003c\/i\u003e 264\u003c\/p\u003e \u003cp\u003e\u003ci\u003e15.3.3 Fouled Coil\u003c\/i\u003e 265\u003c\/p\u003e \u003cp\u003e\u003ci\u003e15.3.4 Valve Stuck in the Fully Closed Position\u003c\/i\u003e 266\u003c\/p\u003e \u003cp\u003e\u003ci\u003e15.3.5 Valve Stuck in the Midway Position\u003c\/i\u003e 267\u003c\/p\u003e \u003cp\u003e\u003ci\u003e15.3.6 Valve Stuck in the Fully Open Position\u003c\/i\u003e 268\u003c\/p\u003e \u003cp\u003e15.4 Commissioning of Air-Handling Units 269\u003c\/p\u003e \u003cp\u003e15.5 Concluding Remarks 272\u003c\/p\u003e \u003cp\u003eReferences 272\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Control of Heat Exchangers 275\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Main Sources of Uncertainty and Practical Considerations 275\u003c\/p\u003e \u003cp\u003e16.2 Design of a Fuzzy FRM-Based Predictive Controller 276\u003c\/p\u003e \u003cp\u003e16.3 Design of a Fuzzy FRM-Based Internal Model Control Scheme 283\u003c\/p\u003e \u003cp\u003e16.4 Concluding Remarks 290\u003c\/p\u003e \u003cp\u003eReferences 290\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Measurement of Spatially Distributed Quantities 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Review of Approaches Suggested for Dealing with Sensor Bias 293\u003c\/p\u003e \u003cp\u003e17.2 An Example Application 294\u003c\/p\u003e \u003cp\u003e\u003ci\u003e17.2.1 Air Temperature Estimation Using a Single-Point Sensor with Bias Correction\u003c\/i\u003e 294\u003c\/p\u003e \u003cp\u003e\u003ci\u003e17.2.2 Air Temperature Estimation Based on Mass and Energy Balances\u003c\/i\u003e 299\u003c\/p\u003e \u003cp\u003e17.3 Using Bias Estimation and Fuzzy Data Fusion to Improve Automated Commissioning in Air-Handling Units 302\u003c\/p\u003e \u003cp\u003e\u003ci\u003e17.3.1 Diagnosis When the Measurement Bias is Estimated Accurately\u003c\/i\u003e 303\u003c\/p\u003e \u003cp\u003e\u003ci\u003e17.3.2 Diagnosis When the Estimate of the Measurement Bias is Inaccurate\u003c\/i\u003e 303\u003c\/p\u003e \u003cp\u003e17.4 Concluding Remarks 305\u003c\/p\u003e \u003cp\u003eReferences 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex 309\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49525388181847,"sku":"9780470688694","price":103.5,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9780470688694.jpg?v=1731860326","url":"https:\/\/bookcurl.com\/products\/monitoring-and-control-of-informationpoor-systems-9780470688694","provider":"Book Curl","version":"1.0","type":"link"}