Muutke küpsiste eelistusi

E-raamat: Monitoring and Control of Information-Poor Systems: An Approach based on Fuzzy Relational Models

4.00/5 (1 hinnangut Goodreads-ist)
(University of Oxford)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 01-Feb-2012
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119945871
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 134,55 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Raamatukogudele
Monitoring and Control of Information-Poor Systems: An Approach based on Fuzzy Relational ModelsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 01-Feb-2012
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119945871
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

In a book that is suitable for a graduate course or for practicing control engineers, Dexter (engineering science, U. of Oxford) describes an approach to monitoring and controlling information-poor systems that is based on fuzzy relational models that generate fuzzy outputs. He covers information-poor systems from the perspectives of analyzing their behavior, control, online learning, and some example applications. The topics include describing and propagating uncertainty, accounting for modeling errors in fuzzy models, incorporating fuzzy inputs, adaptive model-based and model-free control, controlling thermal comfort, and measuring spatially distributed quantities. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)

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 researchers and practitioners working on problems involving mathematical modelling and control.

Preface xi
About the Author xv
Acknowledgements xvii
I ANALYSING THE BEHAVIOUR OF INFORMATION-POOR SYSTEMS
1 Characteristics of Information-Poor Systems
3(10)
1.1 Introduction to Information-Poor Systems
3(4)
1.1.1 Blast Furnaces
3(1)
1.1.2 Container Cranes
3(1)
1.1.3 Cooperative Control Systems
4(1)
1.1.4 Distillation Columns
4(1)
1.1.5 Drug Administration
4(1)
1.1.6 Electrical Power Generation and Distribution
4(1)
1.1.7 Environmental Risk Assessment Systems
4(1)
1.1.8 Financial Investment and Portfolio Selection
5(1)
1.1.9 Health Care Systems
5(1)
1.1.10 Indoor Climate Control
5(1)
1.1.11 NOx Emissions from Gas Turbines and Internal Combustion Engines
6(1)
1.1.12 Penicillin Production Plant
6(1)
1.1.13 Polymerization Reactors
6(1)
1.1.14 Rotary Kilns
6(1)
1.1.15 Solar Power Plant
7(1)
1.1.16 Wastewater Treatment Plant
7(1)
1.1.17 Wood Pulp Production Plant
7(1)
1.2 Main Causes of Uncertainty
7(2)
1.2.1 Sources of Modelling Errors
8(1)
1.2.2 Sources of Measurement Errors
8(1)
1.2.3 Reasons for Poorly Defined Objectives and Constraints
9(1)
1.3 Design in the Face of Uncertainty
9(4)
References
9(4)
2 Describing and Propagating Uncertainty
13(16)
2.1 Methods of Describing Uncertainty
13(2)
2.1.1 Uncertainty Intervals and Probability Distributions
13(1)
2.1.2 Fuzzy Sets and Fuzzy Numbers
14(1)
2.2 Methods of Propagating Uncertainty
15(3)
2.2.1 Interval Arithmetic
15(1)
2.2.2 Statistical Methods
16(1)
2.2.3 Monte Carlo Methods
16(1)
2.2.4 Fuzzy Arithmetic
17(1)
2.3 Fuzzy Arithmetic Using α-Cut Sets and Interval Arithmetic
18(3)
2.4 Fuzzy Arithmetic Based on the Extension Principle
21(3)
2.5 Representing and Propagating Uncertainty Using Pseudo-Triangular Membership Functions
24(3)
2.6 Summary
27(2)
References
27(2)
3 Accounting for Measurement Uncertainty
29(12)
3.1 Measurement Errors
29(1)
3.2 Introduction to Fuzzy Random Variables
29(3)
3.2.1 Definition of a Fuzzy Random Variable
30(1)
3.2.2 Generating Fuzzy Random Variables from a Knowledge of the Random and Systematic Errors
30(2)
3.3 A Hybrid Approach to the Propagation of Uncertainty
32(2)
3.4 Fuzzy Sensor Fusion Based on the Extension Principle
34(4)
3.5 Fuzzy Sensors
38(1)
3.6 Summary
39(2)
References
39(2)
4 Accounting for Modelling Errors in Fuzzy Models
41(22)
4.1 An Introduction to Rule-Based Models
41(1)
4.2 Linguistic Fuzzy Models
41(6)
4.2.1 Fuzzy Rules
41(1)
4.2.2 Fuzzy Inferencing
42(1)
4.2.3 Compositional Rules of Inference
43(4)
4.3 Functional Fuzzy Models
47(1)
4.4 Fuzzy Neural Networks
48(2)
4.5 Methods of Generating Fuzzy Models
50(8)
4.5.1 Modifying Expert Rules to Take Account of Uncertainty
50(6)
4.5.2 Identifying Fuzzy Rules from Data
56(2)
4.6 Defuzzification
58(2)
4.7 Summary
60(3)
References
60(3)
5 Fuzzy Relational Models
63(34)
5.1 Introduction to Fuzzy Relations and Fuzzy Relational Models
63(2)
5.2 Fuzzy FRMs
65(2)
5.3 Methods of Estimating Rule Confidences from Data
67(3)
5.4 Estimating Probability Density Functions from Data
70(16)
5.4.1 Probabilistic Interpretation of RSK Fuzzy Identification
71(7)
5.4.2 Effect of Structural Errors on the Output of a Fuzzy FRM
78(5)
5.4.3 Estimation Based on Limited Amounts of Training Data
83(3)
5.5 Generic Fuzzy Models
86(6)
5.5.1 Identification of Generic Fuzzy Models
87(4)
5.5.2 Reducing the Time Required to Generate the Training Data
91(1)
5.6 Summary
92(5)
References
92(5)
II CONTROL OF INFORMATION-POOR SYSTEMS
6 Fuzzy Decision-Making
97(14)
6.1 Risk Assessment in Information-Poor Systems
97(2)
6.2 Fuzzy Optimization in Information-Poor Systems
99(2)
6.2.1 Fuzzy Goals and Fuzzy Constraints
99(1)
6.2.2 Fuzzy Aggregation Operators
99(1)
6.2.3 Fuzzy Ranking
100(1)
6.3 Multi-Stage Decision-Making
101(5)
6.3.1 Fuzzy Dynamic Programming
102(1)
6.3.2 Branch and Bound
103(3)
6.3.3 Genetic Algorithms
106(1)
6.4 Fuzzy Decision-Making Based on Intuitionistic Fuzzy Sets
106(2)
6.4.1 Definition of an Intuitionistic Fuzzy Set
106(1)
6.4.2 Multi-Attribute Decision-Making Using Intuitionistic Fuzzy Numbers
107(1)
6.5 Summary
108(3)
References
108(3)
7 Predictive Control in Uncertain Systems
111(18)
7.1 Model-Based Predictive Control
111(1)
7.2 Fuzzy Approaches to Model-Based Control of Uncertain Systems
112(3)
7.2.1 Inverse Control of Fuzzy Interval Systems
112(2)
7.2.2 Fuzzy Model-Based Predictive Control
114(1)
7.3 Practical Issues Associated with Multi-Step Fuzzy Decision-Making
115(3)
7.3.1 Limiting the Accumulation of Uncertainty
115(1)
7.3.2 Avoiding Excessive Computational Demands When Using Enumerative Search Optimization
115(1)
7.3.3 Avoiding Excessive Computational Demands When Using Evolutionary Algorithms
116(1)
7.3.4 Handling Infeasibility
117(1)
7.3.5 Choosing the Weighting in Multi-Criteria Cost Functions
117(1)
7.3.6 Dealing with Hard Constraints
118(1)
7.4 A Simplified Approach to Fuzzy FRM-Based Predictive Control
118(4)
7.4.1 The Fuzzy Decision-Maker
119(1)
7.4.2 Conditional Defuzzification
120(2)
7.5 FMPC of an Uncertain Dynamic System Based on a Generic Fuzzy FRM
122(5)
7.6 Summary
127(2)
References
128(1)
8 Incorporating Fuzzy Inputs
129(24)
8.1 Fuzzy Setpoints and Fuzzy Measurements
129(2)
8.1.1 Fuzzy Setpoints
129(1)
8.1.2 Fuzzy Measurements
129(2)
8.2 Fuzzy Measures of the Tracking Error and its Derivative
131(5)
8.3 Inference with Fuzzy Inputs
136(2)
8.4 Fuzzy Output Neural Networks
138(2)
8.5 Modelling Input Uncertainty Using a Fuzzy FRM
140(11)
8.6 Summary
151(2)
References
151(2)
9 Disturbance Rejection in Information-Poor Systems
153(18)
9.1 Rejecting Unmeasured Disturbances in Uncertain Systems
154(3)
9.1.1 Robust Fuzzy Control
154(1)
9.1.2 Feedback Linearization Using a Fuzzy Disturbance Observer
155(1)
9.1.3 Fuzzy Model-Based Internal Model Control
155(2)
9.2 Fuzzy IMC Based on a Fuzzy Output FRM
157(4)
9.3 Rejecting Measured Disturbances in Non-Linear Uncertain Systems
161(1)
9.4 Fuzzy MPC with Feedforward
162(4)
9.5 Summary
166(5)
References
166(5)
III ONLINE LEARNING IN INFORMATION-POOR SYSTEMS
10 Online Model Identification in Information-Poor Environments
171(16)
10.1 Online Fuzzy Identification Schemes
171(5)
10.1.1 Recursive Fuzzy Least-Squares
171(1)
10.1.2 Recursive Forms of the RSK Algorithm
172(4)
10.2 Effect of Poor-Quality and Incomplete Training Data
176(1)
10.3 Ways of Reducing the Computational Demands
177(8)
10.3.1 Evolving Fuzzy Models
177(4)
10.3.2 Hierarchical Fuzzy Models
181(4)
10.4 Summary
185(2)
References
185(2)
11 Adaptive Model-Based Control of Information-Poor Systems
187(24)
11.1 Robust Adaptive Fuzzy Control
187(1)
11.2 Adaptive Fuzzy FRM-Based Predictive Control
188(1)
11.3 Commissioning the Controller
189(3)
11.3.1 Methods of Incorporating Prior Knowledge
189(1)
11.3.2 Initialization Using a Generic Fuzzy FRM
189(3)
11.4 Generating an Optimal Control Signal Using a Partially Trained Model
192(10)
11.4.1 Taking the Amount of Training into Account
192(2)
11.4.2 Incorporating a Secondary Controller
194(7)
11.4.3 Combining the Fuzzy Predictions Generated by More than One Model
201(1)
11.5 Dealing with the Effects of Disturbances
202(7)
11.5.1 Adaptive Feedforward Control Based on an Inaccurate Disturbance Measurement
203(6)
11.6 Summary
209(2)
References
209(2)
12 Adaptive Model-Free Control of Information-Poor Systems
211(18)
12.1 Introduction to Model-Free Adaptive Control of Non-Linear Systems
211(1)
12.2 Fuzzy FRM-Based Direct Adaptive Control
211(2)
12.3 Behaviour in the Presence of a Noisy Measurement of the Plant Output
213(5)
12.4 Behaviour in the Presence of an Unmeasured Disturbance
218(4)
12.5 Accounting for Uncertainty Arising from a Measured Disturbance
222(5)
12.6 Summary
227(2)
References
227(2)
13 Fault Diagnosis in Information-Poor Systems
229(18)
13.1 Introduction to Fault Detection and Isolation in Non-Linear Uncertain Systems
229(4)
13.1.1 Model-Based Methods for Non-Linear Systems
230(2)
13.1.2 Ways of Accounting for Uncertainty
232(1)
13.2 A Fuzzy FRM-Based Fault Diagnosis Scheme
233(9)
13.2.1 Measuring the Similarity of FRMs
234(2)
13.2.2 Accumulating Evidence of Fault-Free or Faulty Operation
236(3)
13.2.3 Generating Robust Generic Models of Faulty Operation
239(1)
13.2.4 Multi-Step Fault Diagnosis
239(3)
13.3 Summary
242(5)
References
243(4)
IV SOME EXAMPLE APPLICATIONS
14 Control of Thermal Comfort
247(14)
14.1 Main Sources of Uncertainty and Practical Considerations
248(1)
14.2 Review of Approaches Suggested for Dealing with the Uncertainty
249(1)
14.3 Design of the Fuzzy FRM-Based Control System
249(5)
14.3.1 The Fuzzy FRM
250(2)
14.3.2 The Fuzzy Cost Functions
252(1)
14.3.3 The Fuzzy Goals
252(2)
14.3.4 The Fuzzy Decision-Maker
254(1)
14.3.5 The Conditional Defuzzifier
254(1)
14.4 Performance of the Thermal Comfort Controller
254(4)
14.5 Concluding Remarks
258(3)
References
259(2)
15 Identification of Faults in Air-Conditioning Systems
261(14)
15.1 Main Sources of Uncertainty and Practical Considerations
261(2)
15.2 Design of a Fuzzy FRM-Based Monitoring System for a Cooling Coil Subsystem
263(1)
15.3 Diagnosis of Known Faults in a Simulated Cooling Coil Subsystem
264(5)
15.3.1 Fault-Free Operation
264(1)
15.3.2 Leaky Valve
264(1)
15.3.3 Fouled Coil
265(1)
15.3.4 Valve Stuck in the Fully Closed Position
266(1)
15.3.5 Valve Stuck in the Midway Position
267(1)
15.3.6 Valve Stuck in the Fully Open Position
268(1)
15.4 Commissioning of Air-Handling Units
269(3)
15.5 Concluding Remarks
272(3)
References
272(3)
16 Control of Heat Exchangers
275(18)
16.1 Main Sources of Uncertainty and Practical Considerations
275(1)
16.2 Design of a Fuzzy FRM-Based Predictive Controller
276(7)
16.3 Design of a Fuzzy FRM-Based Internal Model Control Scheme
283(7)
16.4 Concluding Remarks
290(3)
References
290(3)
17 Measurement of Spatially Distributed Quantities
293(16)
17.1 Review of Approaches Suggested for Dealing with Sensor Bias
293(1)
17.2 An Example Application
294(8)
17.2.1 Air Temperature Estimation Using a Single-Point Sensor with Bias Correction
294(5)
17.2.2 Air Temperature Estimation Based on Mass and Energy Balances
299(3)
17.3 Using Bias Estimation and Fuzzy Data Fusion to Improve Automated Commissioning in Air-Handling Units
302(3)
17.3.1 Diagnosis When the Measurement Bias is Estimated Accurately
303(1)
17.3.2 Diagnosis When the Estimate of the Measurement Bias is Inaccurate
303(2)
17.4 Concluding Remarks
305(4)
References
306(3)
Index 309
Arthur L. Dexter, Department of Engineering Science, University of Oxford, UK Arthur Dexter is Professor of Engineering Science at the University of Oxford where his research focuses on the design and implementation of intelligent control schemes for heating, ventilating and air-conditioning (HVAC) plants in commercial buildings. Having been the principal research investigator on 17 research contracts, he has also had over 100 research papers published in many journals including: Journal of Process Control, Fuzzy Sets & Systems, and Building and Environment, and has been a speaker at multiple international conferences. He is considered to be one of the most important researchers in this area today. Professor Dexter has co-authored two books on the design of microcomputers and co-edited a third book on automated fault detection and diagnosis in buildings.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97811199458712e.html
Märksõnad: