| Fuzzy Decision Making in Modeling and Control Contributor(s): Sousa, Joao M. Costa (Editor), Kaymak, Uzay (Editor) |
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ISBN: 9810248776 ISBN-13: 9789810248772 Publisher: World Scientific Publishing Company OUR PRICE: $106.40 Product Type: Hardcover - Other Formats Published: December 2002 |
| Additional Information |
| BISAC Categories: - Computers | Intelligence (ai) & Semantics - Medical - Computers | Programming - General |
| Dewey: 006.3 |
| LCCN: 2003268760 |
| Series: World Scientific Robotics and Intelligent Systems |
| Physical Information: 0.92" H x 6.36" W x 9.3" (1.37 lbs) 356 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Decision making and control are two fields with distinct methods for solving problems, and yet they are closely related. This book bridges the gap between decision making and control in the field of fuzzy decisions and fuzzy control, and discusses various ways in which fuzzy decision making methods can be applied to systems modeling and control.Fuzzy decision making is a powerful paradigm for dealing with human expert knowledge when one is designing fuzzy model-based controllers. The combination of fuzzy decision making and fuzzy control in this book can lead to novel control schemes that improve the existing controllers in various ways. The following applications of fuzzy decision making methods for designing control systems are considered: - Fuzzy decision making for enhancing fuzzy modeling. The values of important parameters in fuzzy modeling algorithms are selected by using fuzzy decision making.- Fuzzy decision making for designing signal-based fuzzy controllers. The controller mappings and the defuzzification steps can be obtained by decision making methods.- Fuzzy design and performance specifications in model-based control. Fuzzy constraints and fuzzy goals are used.- Design of model-based controllers combined with fuzzy decision modules. Human operator experience is incorporated for the performance specification in model-based control.The advantages of bringing together fuzzy control and fuzzy decision making are shown with multiple examples from real and simulated control systems. |