Hellendoorn / Driankov Fuzzy Model Identification

Selected Approaches
1997
ISBN: 978-3-642-60767-7
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 319 Seiten, eBook

During the past few years two principally different approaches to the design of fuzzy controllers have emerged: heuristics-based design and model-based design. The main motivation for the heuristics-based design is given by the fact that many industrial processes are still controlled in one of the following two ways: - The process is controlled manually by an experienced operator. - The process is controlled by an automatic control system which needs manual, on-line 'trimming' of its parameters by an experienced operator. In both cases it is enough to translate in terms of a set of fuzzy if-then rules the operator's manual control algorithm or manual on-line 'trimming' strategy in order to obtain an equally good, or even better, wholly automatic fuzzy control system. This implies that the design of a fuzzy controller can only be done after a manual control algorithm or trimming strategy exists. It is admitted in the literature on fuzzy control that the heuristics-based approach to the design of fuzzy controllers is very difficult to apply to multiple-inputjmultiple-output control problems which represent the largest part of challenging industrial process control applications. Furthermore, the heuristics-based design lacks systematic and formally verifiable tuning tech niques. Also, studies of the stability, performance, and robustness of a closed loop system incorporating a heuristics-based fuzzy controller can only be done via extensive simulations.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Hellendoorn, Hans

Driankov, Dimiter

Inhaltsverzeichnis

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.