Fuzzy Model Identification

Selected Approaches
Ed. by Hans Hellendoorn and Dimiter Driankov

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-540-62721-0
• 1997.
• Seitenzahl: 344
• Erscheinungstermin: 16. Oktober 1997
• Englisch
• Abmessung: 235mm x 155mm x 19mm
• Gewicht: 516g
• ISBN-13: 9783540627210
• ISBN-10: 3540627219
• Artikelnr.: 26704831

Inhaltsangabe

General Overview.- Fuzzy Identification from a Grey Box Modeling Point of View.- Clustering Methods.- Constructing Fuzzy Models by Product Space Clustering.- Identification of Takagi-Sugeno Fuzzy Models via Clustering and Hough Transform.- Rapid Prototyping of Fuzzy Models Based on Hierarchical Clustering.- Neural Networks.- Fuzzy Identification Using Methods of Intelligent Data Analysis.- Identification of Singleton Fuzzy Models via Fuzzy Hyperrectangular Composite NN.- Genetic Algorithms.- Identification of Linguistic Fuzzy Models by Means of Genetic Algorithms.- Optimization of Fuzzy Models by Global Numeric Optimization.- Artificial Intelligence.- Identification of Linguistic Fuzzy Models Based on Learning.