This carefully edited collection of recent works in fuzzy model identification opens the field to conventional control theorists as a complement to existing approaches, provides practicing engineers with new techniques, and emphasizes opportunities for new theory by bringing together different methods to identify the same types of fuzzy models. In control engineering, mathematical models are often constructed without using system data (white-box models) or using data but no insight (black-box models). The authors in this volume combine white- and black-box models chosen from types of structures known to be flexible and successful in applications. They use the same notation and terminology, and each describes a model with an identification technique and gives a practical example to show how the method works.
Edited by:
Hans Hellendoorn, Dimiter Driankov Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K Country of Publication: Germany Edition: Softcover reprint of the original 1st ed. 1997 Dimensions:
Height: 235mm,
Width: 155mm,
Spine: 18mm
Weight: 525g ISBN:9783540627210 ISBN 10: 3540627219 Pages: 319 Publication Date:16 October 1997 Audience:
College/higher education
,
Professional and scholarly
,
Further / Higher Education
,
Undergraduate
Format:Paperback Publisher's Status: Active
General Overview.- Fuzzy Identification from a Grey Box Modeling Point of View.- 1. Introduction.- 2. System Identification.- 3. Fuzzy Modeling Framework.- 4. Fuzzy Identification Based on Prior Knowledge.- 5. Example - Tank Level Modeling.- 6. Practical Aspects.- 7. Conclusions and Future Work.- References.- Clustering Methods.- Constructing Fuzzy Models by Product Space Clustering.- 1. Introduction.- 2. Overview of Fuzzy Models.- 3. Structure Selection for Modeling of Dynamic Systems.- 4. Fuzzy Clustering.- 5. Deriving Takagi-Sugeno Fuzzy Models.- 6. Example: pH Neutralization.- 7. Practical Considerations and Concluding Remarks.- A. The Gustafson-Kessel Algorithm - MATLAB Implementation.- References.- Identification of Takagi-Sugeno Fuzzy Models via Clustering and Hough Transform.- 1. Introduction.- 2. The Identification Method.- 3. Example 1.- 4. Example 2.- 5. Summary of the Identification Procedure.- 6. Practical Considerations and Concluding Remarks.- References.- Rapid Prototyping of Fuzzy Models Based on Hierarchical Clustering.- 1. Introduction.- 2. The Fuzzy C-Means Algorithm.- 3. Using Hierarchical Clustering to Preprocess Data.- 4. Rapid Prototyping of Approximative Fuzzy Models.- 5. Rapid Prototyping of Descriptive Fuzzy Models.- 6. Examples.- 7. Practical Considerations and Concluding Remarks.- A. Proofs of Propositions.- References.- Neural Networks.- Fuzzy Identification Using Methods of Intelligent Data Analysis.- 1. Introduction.- 2. Neuro-Fuzzy Methods.- 3. Density Estimation.- 4. Fuzzy Clustering.- 5. Conclusion.- A. From Rules to Networks.- B. Learning Rule for RBF Networks.- C.Update Equations for Gaussian Mixtures.- D. Adaptation Algorithm for Fuzzy Clustering.- References.- Identification of Singleton Fuzzy Models via Fuzzy Hyperrectangular Composite NN.- 1. Introduction.- 2. Classification of Fuzzy Models.- 3. Fuzzy Neural Networks.- 4. Identification of Singleton Fuzzy Models.- 5. Simulation Results.- 6. Practical Considerations and Concluding Remarks.- References.- Genetic Algorithms.- Identification of Linguistic Fuzzy Models by Means of Genetic Algorithms.- 1. Introduction.- 2. Evolutionary Algorithms and Genetic Fuzzy Systems.- 3. The Fuzzy Model Identification Problem.- 4. The Genetic Fuzzy Identification Method.- 5. Example.- 6. Practical Considerations and Concluding Remarks.- References.- Optimization of Fuzzy Models by Global Numeric Optimization.- 1. Introduction.- 2. Theoretical Aspects of Fuzzy Models.- 3. The Fuzzy Identification Method.- 4. Simulation Results.- 5. Practical Aspects.- References.- Artificial Intelligence.- Identification of Linguistic Fuzzy Models Based on Learning.- 1. Introduction.- 2. Basic Concepts and Notation.- 3. The Identification Problem.- 4. The Fuzzy Identification Method.- 5. Numeric Examples.- 6. Practical Aspects and Concluding Remarks.- References.