საქართველოს ტექნიკური უნივერსიტეტის ნიკო მუსხელიშვილის სახელობის საუნივერსიტეტო ბიბლიოთეკა

Niko Muskhelishvili University Library of Georgian Technical University

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Fuzzy model identification for control / János Abonyi.

By: Material type: TextTextPublication details: Boston : Birkhäuser, c2003.Description: x, 273 p. : ill. ; 24 cmISBN:
  • 0817642382 (alk. paper)
  • 3764342382 (alk. paper)
Subject(s): DDC classification:
  • 629.8 21
LOC classification:
  • TJ213 .A222 2003
Contents:
1. Introduction -- 2. Fuzzy Model Structures and their Analysis -- 2.1. Introduction to Fuzzy Modeling -- 2.2. Takagi-Sugeno Fuzzy Models (TS) -- 2.3. Fuzzy Models with Multivariate Membership Functions (MMF) -- 2.4. Input Reduction of Fuzzy Models -- 2.5. Fuzzy Model Inversion -- 2.6. Linearization and Derivatives of Fuzzy Models -- 3. Fuzzy Models of Dynamical Systems -- 3.1. Data-Driven Empirical Modeling -- 3.2. TS Fuzzy Models of Dynamical Systems -- 3.3. TS Fuzzy Models of MIMO Systems -- 3.4. Hybrid Fuzzy Convolution Model (HFCM) -- 3.5. Fuzzy Hammerstein Model (FH) -- 4. Fuzzy Model Identification -- 4.1. Identification as an Optimization Problem -- 4.2. Consequent Parameter Identification -- 4.3. Model Structure Identification -- 4.4. Antecedent Membership Function Identification -- 4.5. MMF Fuzzy Model Identification -- 4.6. Hybrid Fuzzy Convolution Model Identification -- 4.7. Fuzzy Hammerstein Model Identification --
5. Fuzzy Model based Control -- 5.1. Introduction to Fuzzy Control -- 5.2. Inverse Fuzzy Model based (Adaptive) Control -- 5.3. Introduction to Model Predictive Control -- 5.4. TS Fuzzy Model based Predictive Control -- 5.5. MIMO Fuzzy model based Predictive Control -- 5.6. HFCM based Predictor Corrector Controller -- 5.7. HFCM based Predictive Control -- 5.8. Fuzzy Hammerstein Model based Predictive Control -- 5.9. Grey-Box TS Fuzzy Model based Adaptive Control -- A. Process Models Used for Case Studies.
Review: "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.".Summary: "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.".Summary: "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.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
წიგნები / Books წიგნები / Books ცენტრალური ბიბლიოთეკა / Central library ჰუმანიტარული დარ. / Humanitarian hall 519.711.3 / 3 (Browse shelf(Opens below)) K - 12747 Available 2014-5871

Price 294 L.

Includes bibliographical references (p. 249-270) and index.

1. Introduction -- 2. Fuzzy Model Structures and their Analysis -- 2.1. Introduction to Fuzzy Modeling -- 2.2. Takagi-Sugeno Fuzzy Models (TS) -- 2.3. Fuzzy Models with Multivariate Membership Functions (MMF) -- 2.4. Input Reduction of Fuzzy Models -- 2.5. Fuzzy Model Inversion -- 2.6. Linearization and Derivatives of Fuzzy Models -- 3. Fuzzy Models of Dynamical Systems -- 3.1. Data-Driven Empirical Modeling -- 3.2. TS Fuzzy Models of Dynamical Systems -- 3.3. TS Fuzzy Models of MIMO Systems -- 3.4. Hybrid Fuzzy Convolution Model (HFCM) -- 3.5. Fuzzy Hammerstein Model (FH) -- 4. Fuzzy Model Identification -- 4.1. Identification as an Optimization Problem -- 4.2. Consequent Parameter Identification -- 4.3. Model Structure Identification -- 4.4. Antecedent Membership Function Identification -- 4.5. MMF Fuzzy Model Identification -- 4.6. Hybrid Fuzzy Convolution Model Identification -- 4.7. Fuzzy Hammerstein Model Identification --

5. Fuzzy Model based Control -- 5.1. Introduction to Fuzzy Control -- 5.2. Inverse Fuzzy Model based (Adaptive) Control -- 5.3. Introduction to Model Predictive Control -- 5.4. TS Fuzzy Model based Predictive Control -- 5.5. MIMO Fuzzy model based Predictive Control -- 5.6. HFCM based Predictor Corrector Controller -- 5.7. HFCM based Predictive Control -- 5.8. Fuzzy Hammerstein Model based Predictive Control -- 5.9. Grey-Box TS Fuzzy Model based Adaptive Control -- A. Process Models Used for Case Studies.

"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.".

"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.

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