| Description |
x, 273 pages : illustrations ; 24 cm |
| Series |
Springer Nature Book Archives Millennium (2000-2004)
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| Contents |
Machine generated contents note: Preface vii --1 Introduction 1 -- 1.1 Fuzzy Modeling with the Use of Prior Knowledge 1 -- 1.2 Fuzzy model-based Control 16 -- 1.3 Illustrative Examples 19 -- 1.4 Summary 19 --2 Fuzzy Model Structures and their Analysis 23 -- 2.1 Introduction to Fuzzy Modeling 23 -- 2.2 Takagi-Sugeno Fuzzy Models (TS) 29 -- 2.2.1 Structure of Zero- and First-order TS Fuzzy Models 30 -- 2.2.2 Related Modeling Paradigms 36 -- 2.3 Fuzzy Models with Multivariate Membership -- Functions (MMF) 39 -- 2.4 Input Reduction of Fuzzy Models 44 -- 2.4.1 TS Fuzzy Model Reduction 44 -- 2.4.2 MMF Fuzzy Model Reduction 45 -- 2.5 Fuzzy Model Inversion 46 -- 2.5.1 TS Fuzzy Model Inversion 46 -- 2.5.2 MMF Fuzzy Model Inversion 49 -- 2.6 Linearization and Derivatives of Fuzzy Models 50 -- 2.6.1 Derivatives of TS Fuzzy Models 50 -- 2.6.2 Derivatives of MMF Fuzzy Models 52 --3 Fuzzy Models of Dynamical Systems 53 -- 3.1 Data-Driven Empirical Modeling53 -- 3.1.1 General Model Structures55 -- 3.1.2 Special Model Structures57 -- 3.2 TS Fuzzy Models of Dynamical Systems60 -- 3.2.1 Structure of the NARX TS Fuzzy Model 60 -- 3.2.2 Steady-state Behavior and Local Stability 63 -- 3.2.3 Extraction of Linear Dynamical Models 65 -- 3.3 TS Fuzzy Models of MIMO Systems 71 -- 3.4 Hybrid Fuzzy Convolution Model (HFCM) 73 -- 3.5 Fuzzy Hammerstein Model (FH) 80 --4 Fuzzy Model Identification 87 -- 4.1 Identification as an Optimization Problem 87 -- 4.2 Consequent Parameter Identification 91 -- 4.2.1 Local and Global Identification 91 -- 4.2.2 MIMO TS Fuzzy Model Identification 102 -- 4.2.3 Grey-Box TS Fuzzy Model Identification 107 -- 4.2.4 Prior Knowledge based Parameter Constraints 109 -- 4.3 Model Structure Identification 118 -- 4.4 Antecedent Membership Function Identification 122 -- 4.5 MMF Fuzzy Model Identification 133 -- 4.5.1 Identification of the Consequent Parameters 133 -- 4.5.2 Step-wise Rule Construction Algorithm 134 -- 4.5.3 Grey-Box MMF Fuzzy Model Identification 139 -- 4.6 Hybrid Fuzzy Convolution Model Identification 145 -- 4.7 Fuzzy Hammerstein Model Identification 150 --5 Fuzzy Model based Control 165 -- 5.1 Introduction to Fuzzy Control 165 -- 5.2 Inverse Fuzzy Model based (Adaptive) Control 169 -- 5.3 Introduction to Model Predictive Control 180 -- 5.4 TS Fuzzy Model based Predictive Control 187 -- 5.5 MIMO Fuzzy model based Predictive Control 200 -- 5.6 HFCM based Predictor Corrector Controller 204 -- 5.7 HFCM based Predictive Control 211 -- 5.8 Fuzzy Hammerstein Model based Predictive Control 216 -- 5.9 Grey-Box TS Fuzzy Model based Adaptive Control 226 --A Process Models Used for Case Studies 241 -- A.1 Model of the pH Process 241 -- A.2 Electrical Water-Heater 243 -- A.3 Distillation Column 245 -- A.4 Model of the liquid level rig 247 --References 249 --Index 271 |
| Summary |
"This book presents new approaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effective use of heterogeneous information in the form of numerical data, qualitative knowledge, and first principle models. The main methods and techniques are illustrated through several simulated examples and real-world applications from chemical and process engineering practice." "Supporting MATLAB and Simulink files, available at the website www.fmt.vein.hu/softcomp, create a computational platform for exploration and illustration of many concepts and algorithms presented in the book." |
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"The book is aimed primarily at researchers, practitioners, and professionals in process control and identification, but it is also accessible to graduate students in electrical, chemical, and process engineering. Technical prerequisites include an undergraduate-level knowledge of control theory and linear algebra. Additional familiarity with fuzzy systems is helpful but not required."--BOOK JACKET |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Automatic control -- Mathematical models.
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Fuzzy systems.
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System identification.
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| LC no. |
2002038615 |
| ISBN |
0817642382 alkaline paper |
|
3764342382 alkaline paper |
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