Ebook: Fuzzy Model Identification: Selected Approaches

This carefully edited volume presents a collection of recent works in fuzzy model identification. It opens the field of fuzzy identification to conventional control theorists as a complement to existing approaches, provides practicing control engineers with the algorithmic and practical aspects of a set of new identification techniques, and emphasizes opportunities for a more systematic and coherent theory of fuzzy identification by bringing together methods based on different techniques but aiming at the identification of the same types of fuzzy models.
In control engineering, mathematical models are often constructed, for example based on differential or difference equations or derived from physical laws without using system data (white-box models) or using data but no insight (black-box models). In this volume the authors choose a combination of these models from types of structures that are known to be flexible and successful in applications. They consider Mamdani, Takagi-Sugeno, and singleton models, employing such identification methods as clustering, neural networks, genetic algorithms, and classical learning.
All authors use the same notation and terminology, and each describes the model to be identified and the identification technique with algorithms that will help the reader to apply the presented methods in his or her own environment to solve real-world problems. Furthermore, each author gives a practical example to show how the presented method works, and deals with the issues of prior knowledge, model complexity, robustness of the identification method, and real-world applications.

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This carefully edited volume presents a collection of recent works in fuzzy model identification. It opens the field of fuzzy identification to conventional control theorists as a complement to existing approaches, provides practicing control engineers with the algorithmic and practical aspects of a set of new identification techniques, and emphasizes opportunities for a more systematic and coherent theory of fuzzy identification by bringing together methods based on different techniques but aiming at the identification of the same types of fuzzy models.
In control engineering, mathematical models are often constructed, for example based on differential or difference equations or derived from physical laws without using system data (white-box models) or using data but no insight (black-box models). In this volume the authors choose a combination of these models from types of structures that are known to be flexible and successful in applications. They consider Mamdani, Takagi-Sugeno, and singleton models, employing such identification methods as clustering, neural networks, genetic algorithms, and classical learning.
All authors use the same notation and terminology, and each describes the model to be identified and the identification technique with algorithms that will help the reader to apply the presented methods in his or her own environment to solve real-world problems. Furthermore, each author gives a practical example to show how the presented method works, and deals with the issues of prior knowledge, model complexity, robustness of the identification method, and real-world applications.

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This carefully edited volume presents a collection of recent works in fuzzy model identification. It opens the field of fuzzy identification to conventional control theorists as a complement to existing approaches, provides practicing control engineers with the algorithmic and practical aspects of a set of new identification techniques, and emphasizes opportunities for a more systematic and coherent theory of fuzzy identification by bringing together methods based on different techniques but aiming at the identification of the same types of fuzzy models.
In control engineering, mathematical models are often constructed, for example based on differential or difference equations or derived from physical laws without using system data (white-box models) or using data but no insight (black-box models). In this volume the authors choose a combination of these models from types of structures that are known to be flexible and successful in applications. They consider Mamdani, Takagi-Sugeno, and singleton models, employing such identification methods as clustering, neural networks, genetic algorithms, and classical learning.
All authors use the same notation and terminology, and each describes the model to be identified and the identification technique with algorithms that will help the reader to apply the presented methods in his or her own environment to solve real-world problems. Furthermore, each author gives a practical example to show how the presented method works, and deals with the issues of prior knowledge, model complexity, robustness of the identification method, and real-world applications.
Content:
Front Matter....Pages I-XXI
Front Matter....Pages 1-1
Fuzzy Identification from a Grey Box Modeling Point of View....Pages 3-50
Front Matter....Pages 51-51
Constructing Fuzzy Models by Product Space Clustering....Pages 53-90
Identification of Takagi-Sugeno Fuzzy Models via Clustering and Hough Transform....Pages 91-119
Rapid Prototyping of Fuzzy Models Based on Hierarchical Clustering....Pages 121-161
Front Matter....Pages 163-163
Fuzzy Identification Using Methods of Intelligent Data Analysis....Pages 165-191
Identification of Singleton Fuzzy Models via Fuzzy Hyperrectangular Composite NN....Pages 193-212
Front Matter....Pages 213-213
Identification of Linguistic Fuzzy Models by Means of Genetic Algorithms*....Pages 215-250
Optimization of Fuzzy Models by Global Numeric Optimization....Pages 251-278
Front Matter....Pages 279-279
Identification of Linguistic Fuzzy Models Based on Learning....Pages 281-319

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This carefully edited volume presents a collection of recent works in fuzzy model identification. It opens the field of fuzzy identification to conventional control theorists as a complement to existing approaches, provides practicing control engineers with the algorithmic and practical aspects of a set of new identification techniques, and emphasizes opportunities for a more systematic and coherent theory of fuzzy identification by bringing together methods based on different techniques but aiming at the identification of the same types of fuzzy models.
In control engineering, mathematical models are often constructed, for example based on differential or difference equations or derived from physical laws without using system data (white-box models) or using data but no insight (black-box models). In this volume the authors choose a combination of these models from types of structures that are known to be flexible and successful in applications. They consider Mamdani, Takagi-Sugeno, and singleton models, employing such identification methods as clustering, neural networks, genetic algorithms, and classical learning.
All authors use the same notation and terminology, and each describes the model to be identified and the identification technique with algorithms that will help the reader to apply the presented methods in his or her own environment to solve real-world problems. Furthermore, each author gives a practical example to show how the presented method works, and deals with the issues of prior knowledge, model complexity, robustness of the identification method, and real-world applications.
Content:
Front Matter....Pages I-XXI
Front Matter....Pages 1-1
Fuzzy Identification from a Grey Box Modeling Point of View....Pages 3-50
Front Matter....Pages 51-51
Constructing Fuzzy Models by Product Space Clustering....Pages 53-90
Identification of Takagi-Sugeno Fuzzy Models via Clustering and Hough Transform....Pages 91-119
Rapid Prototyping of Fuzzy Models Based on Hierarchical Clustering....Pages 121-161
Front Matter....Pages 163-163
Fuzzy Identification Using Methods of Intelligent Data Analysis....Pages 165-191
Identification of Singleton Fuzzy Models via Fuzzy Hyperrectangular Composite NN....Pages 193-212
Front Matter....Pages 213-213
Identification of Linguistic Fuzzy Models by Means of Genetic Algorithms*....Pages 215-250
Optimization of Fuzzy Models by Global Numeric Optimization....Pages 251-278
Front Matter....Pages 279-279
Identification of Linguistic Fuzzy Models Based on Learning....Pages 281-319
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