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Fuzzy Model Identification for Control by Janos Abonyi/ 1st Ed/ 2003
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- Buy It NowFuzzy Model Identification for Control by Janos Abonyi/ 1st Ed/ 2003
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Item specifics
- Condition
- Special Attributes
- 1st Edition
- ISBN
- 9780817642389
- Subject Area
- Technology & Engineering, Science
- Publication Name
- Fuzzy Model Identification for Control
- Publisher
- Birkhäuser Boston
- Item Length
- 9.3 in
- Subject
- Automation, System Theory, Electrical, Chemistry / Industrial & Technical
- Publication Year
- 2003
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Weight
- 45.2 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xi, 273 Pages
About this product
Product Identifiers
Publisher
Birkhäuser Boston
ISBN-10
0817642382
ISBN-13
9780817642389
eBay Product ID (ePID)
1991531
Product Key Features
Number of Pages
Xi, 273 Pages
Publication Name
Fuzzy Model Identification for Control
Language
English
Subject
Automation, System Theory, Electrical, Chemistry / Industrial & Technical
Publication Year
2003
Type
Textbook
Subject Area
Technology & Engineering, Science
Format
Hardcover
Dimensions
Item Weight
45.2 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2002-038615
Dewey Edition
21
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
62938
Table Of Content
1 Introduction.- 1.1 Fuzzy Modeling with the Use of Prior Knowledge.- 1.2 Fuzzy model-based Control.- 1.3 Illustrative Examples.- 1.4 Summary.- 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.- A.l Model of the pH Process.- A.2 Electrical Water-Heater.- A.3 Distillation Column.- A.4 Model of the liquid level rig.- References.
Synopsis
This book presents new approaches to constructing fuzzy models for model-based control. Simulated examples and real-world applications from chemical and process engineering illustrate the main methods and techniques. Supporting MATLAB and Simulink files create a computational platform for exploration of the concepts and algorithms., Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima- tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in- formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap- proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec- tive use of heterogenous information in the form of numerical data, qualita- tive knowledge and first-principle models. By exploiting the mathematical properties of the proposed model structures, such as invertibility and local linearity, new control algorithms will be presented., Overview Since the early 1990s, fuzzy modeling and identification from process data have been and continue to be an evolving subject of interest. Although the application of fuzzy models proved to be effective for the approxima tion of uncertain nonlinear processes, the data-driven identification offuzzy models alone sometimes yields complex and unrealistic models. Typically, this is due to the over-parameterization of the model and insufficient in formation content of the identification data set. These difficulties stem from a lack of initial a priori knowledge or information about the system to be modeled. To solve the problem of limited knowledge, in the area of modeling and identification, there is a tendency to blend information of different natures to employ as much knowledge for model building as possible. Hence, the incorporation of different types of a priori knowledge into the data-driven fuzzy model generation is a challenging and important task. Motivated by our research into this topic, our book presents new ap proaches to the construction of fuzzy models for model-based control. New model structures and identification algorithms are described for the effec tive use of heterogenous information in the form of numerical data, qualita tive knowledge and first-principle models. By exploiting the mathematical properties of the proposed model structures, such as invertibility and local linearity, new control algorithms will be presented., This book presents new approaches to the construction of fuzzy models for model-based control. The main methods and techniques are illustrated through simulated examples and real-world applications from chemical and process engineering. Supporting MATLAB and Simulink files--available at www.fmt.vein.hu/softcomp--create a computational platform for exploration and illustration of concepts and algorithms presented in the book. Aimed at researchers, practitioners, and professionals in process control and identification, but also accessible to grad students in electrical, chemical, and process engineering.
LC Classification Number
TJ212-225
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- Jul 13, 2020
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Verified purchase: YesCondition: Pre-OwnedSold by: acezilla
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