Fuzzy modeling for control:
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boston [u.a.]
Kluwer Acad. Publ.
1998
|
| Series: | International series in intelligent technologies
12 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XII, 260 S. Ill., graph. Darst. |
| ISBN: | 0792381548 |
Staff View
MARC
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| 100 | 1 | |a Babuska, Robert |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Fuzzy modeling for control |c Robert Babuška |
| 264 | 1 | |a Boston [u.a.] |b Kluwer Acad. Publ. |c 1998 | |
| 300 | |a XII, 260 S. |b Ill., graph. Darst. | ||
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| 490 | 1 | |a International series in intelligent technologies |v 12 | |
| 650 | 7 | |a Fuzzy (inteligência artificial) |2 larpcal | |
| 650 | 4 | |a Control theory | |
| 650 | 4 | |a Fuzzy systems | |
| 650 | 4 | |a Intelligent control systems | |
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Record in the Search Index
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| adam_text | Contents
Preface xi
Acknowledgments xiii
1. INTRODUCTION 1
1.1 Modeling and Identification of Complex Systems 1
1.2 Different Modeling Paradigms 2
1.3 Fuzzy Modeling 3
1.4 Fuzzy Identification 4
1.5 Control Design Based on Fuzzy Models 6
1.6 Outline of the Book 6
2. FUZZY MODELING 9
2.1 Linguistic Fuzzy Models 10
2.1.1 Linguistic Terms and Variables 11
2.1.2 Antecedent Propositions 12
2.1.3 Linguistic Hedges 13
2.1.4 Inference in the Linguistic Model 14
2.1.5 Defuzzification 20
2.1.6 Fuzzy Implication versus Mamdani Inference 21
2.1.7 Rule Chaining 23
2.1.8 Singleton Model 24
2.2 Fuzzy Relational Models 25
2.3 Takagi Sugeno Models 29
2.3.1 Inference in the TS Model 30
2.3.2 Analysis of the TS Inference 32
2.3.3 Alternative Interpolation Scheme for the TS Model 36
2.4 Constructing Fuzzy Models 39
2.4.1 Knowledge based Approach 41
2.4.2 Data driven Methods 42
2.5 Summary and Concluding Remarks 46
3. FUZZY CLUSTERING ALGORITHMS 49
3.1 Cluster Analysis 50
3.1.1 The Data 50
viii FUZZY MODELING FOR CONTROL
3.1.2 What Are Clusters? 50
3.1.3 Clustering Methods 51
3.2 Hard and Fuzzy Partitions 52
3.2.1 Hard Partition 52
3.2.2 Fuzzy Partition 54
3.2.3 Possibilistic Partition 55
3.3 Fuzzy c Means Clustering 55
3.3.1 The Fuzzy c Means Functional 56
3.3.2 The Fuzzy c Means Algorithm 56
3.3.3 Inner product Norms 58
3.4 Clustering with Fuzzy Covariance Matrix 60
3.4.1 Gustafson Kessel Algorithm 60
3.4.2 Fuzzy Maximum Likelihood Estimates Clustering 63
3.5 Clustering with Linear Prototypes 64
3.5.1 Fuzzy c Varieties 66
3.5.2 Fuzzy c Elliptotypes 66
3.5.3 Fuzzy c Regression Models 68
3.6 Possibilistic Clustering 69
3.7 Determining the Number of Clusters 72
3.8 Data Normalization 72
3.9 Summary and Concluding Remarks 74
4. PRODUCT SPACE CLUSTERING FOR IDENTIFICATION 75
4.1 Outline of the Approach 75
4.2 Structure Selection 77
4.2.1 The Nonlinear Regression Problem 78
4.2.2 Input output Black box Models 79
4.2.3 State space Framework 82
4.2.4 Semi mechanistic Modeling 82
4.3 Identification by Product space Clustering 83
4.4 Choice of Clustering Algorithms 88
4.4.1 Clustering with Adaptive Distance Measure 88
4.4.2 Fuzzy c lines and c elliptotypes 91
4.4.3 Fuzzy c regression Models 93
4.5 Determining the Number of Clusters 94
4.5.1 Cluster Validity Measures 94
4.5.2 Compatible Cluster Merging 98
4.6 Summary and Concluding Remarks 107
5. CONSTRUCTING FUZZY MODELS FROM PARTITIONS 109
5.1 Takagi Sugeno Fuzzy Models 109
5.1.1 Generating Antecedent Membership Functions 110
5.1.2 Estimating Consequent Parameters 118
5.1.3 Rule Base Simplification 129
5.1.4 Linguistic Approximation 134
5.1.5 Examples 135
5.1.6 Practical Considerations 142
Contents ix
5.2 Linguistic and Relational Models 144
5.2.1 Extraction of Antecedent Membership Functions 145
5.2.2 Estimation of Consequent Parameters 146
5.2.3 Convertion of Singleton Model into Relational Model 148
5.2.4 Estimation of Fuzzy Relations from Data 150
5.3 Low level Fuzzy Relational Models 153
5.4 Summary and Concluding Remarks 160
6. FUZZY MODELS IN NONLINEAR CONTROL 161
6.1 Control by Inverting Fuzzy Models 162
6.1.1 Singleton Model 162
6.1.2 Inversion of the Singleton Model 164
6.1.3 Compensation of Disturbances and Modeling Errors 171
6.2 Predictive Control 173
6.2.1 Basic Concepts 174
6.2.2 Fuzzy Models in MBPC 176
6.2.3 Predictive Control with Fuzzy Objective Function 183
6.3 Example: Heat Transfer Process 186
6.3.1 Fuzzy Modeling 186
6.3.2 Inverse Model Control 187
6.3.3 Predictive Control 188
6.3.4 Adaptive Predictive Control 191
6.4 Example: pH Control 192
6.5 Summary and Concluding Remarks 193
7. APPLICATIONS 197
7.1 Performance Prediction of a Rock cutting Trencher 198
7.1.1 The Trencher and Its Performance 198
7.1.2 Knowledge based Fuzzy Model 198
7.1.3 Applied Methods and Algorithms 203
7.1.4 Model Validation and Results 205
7.1.5 Discussion 206
7.2 Pressure Modeling and Control 208
7.2.1 Process Description 208
7.2.2 Data Collection 209
7.2.3 SISO Fuzzy Model 209
7.2.4 MISO Fuzzy Model 212
7.2.5 Predictive Control Based on the Fuzzy Model 213
7.2.6 Discussion 214
7.3 Fuzzy Modeling of Enzymatic Penicillin—G Conversion 216
7.3.1 Introduction 216
7.3.2 Process Description 218
7.3.3 Experimental Set up 220
7.3.4 Fuzzy Modeling 220
7.3.5 Semi mechanistic Model 224
7.3.6 Discussion 224
7.4 Summary and Concluding Remarks 225
X FUZZY MODELING FOR CONTROL
Appendices 226
A Basic Concepts of Fuzzy Set Theory 227
A.I Fuzzy Sets 227
A.2 Membership Functions 227
A3 Basic Definitions 228
A.4 Operations on Fuzzy Sets 229
A.5 Fuzzy Relations 230
A.6 Projections and Cylindrical Extensions 230
B Fuzzy Modeling and Identification Toolbox for MATLAB 233
B.I Toolbox Structure 233
B.2 Identification of MIMO Dynamic Systems 233
B.3 Matlab implementation 234
C Symbols and Abbreviations 239
References 243
Author Index 253
Subject Index 257
|
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| indexdate | 2024-07-09T18:23:45Z |
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| isbn | 0792381548 |
| language | English |
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| physical | XII, 260 S. Ill., graph. Darst. |
| publishDate | 1998 |
| publishDateSearch | 1998 |
| publishDateSort | 1998 |
| publisher | Kluwer Acad. Publ. |
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| series | International series in intelligent technologies |
| series2 | International series in intelligent technologies |
| spelling | Babuska, Robert Verfasser aut Fuzzy modeling for control Robert Babuška Boston [u.a.] Kluwer Acad. Publ. 1998 XII, 260 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier International series in intelligent technologies 12 Fuzzy (inteligência artificial) larpcal Control theory Fuzzy systems Intelligent control systems Real-time control Fuzzy-Regelung (DE-588)4395755-9 gnd rswk-swf Fuzzy-Regelung (DE-588)4395755-9 s DE-604 International series in intelligent technologies 12 (DE-604)BV010552630 12 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008278843&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Babuska, Robert Fuzzy modeling for control International series in intelligent technologies Fuzzy (inteligência artificial) larpcal Control theory Fuzzy systems Intelligent control systems Real-time control Fuzzy-Regelung (DE-588)4395755-9 gnd |
| subject_GND | (DE-588)4395755-9 |
| title | Fuzzy modeling for control |
| title_auth | Fuzzy modeling for control |
| title_exact_search | Fuzzy modeling for control |
| title_full | Fuzzy modeling for control Robert Babuška |
| title_fullStr | Fuzzy modeling for control Robert Babuška |
| title_full_unstemmed | Fuzzy modeling for control Robert Babuška |
| title_short | Fuzzy modeling for control |
| title_sort | fuzzy modeling for control |
| topic | Fuzzy (inteligência artificial) larpcal Control theory Fuzzy systems Intelligent control systems Real-time control Fuzzy-Regelung (DE-588)4395755-9 gnd |
| topic_facet | Fuzzy (inteligência artificial) Control theory Fuzzy systems Intelligent control systems Real-time control Fuzzy-Regelung |
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