Modeling Uncertainty with Fuzzy Logic [electronic resource] :With Recent Theory and Applications / by Asli Celikyilmaz, I. Burhan Türksen.
by Celikyilmaz, Asli [author.]; Türksen, I. Burhan [author.]; SpringerLink (Online service).
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Item type | Current location | Call number | Status | Date due | Barcode |
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TA640-643 (Browse shelf) | Available | ||||
Long Loan | MAIN LIBRARY | TA329-348 (Browse shelf) | Available |
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TA329-348 Fuzzy Preference Ordering of Interval Numbers in Decision Problems | TJ210.2-211.495 Modeling and Control for Efficient Bipedal Walking Robots | TA329-348 Rough Set Theory: A True Landmark in Data Analysis | TA329-348 Modeling Uncertainty with Fuzzy Logic | TA329-348 Analytical Methods in Fuzzy Modeling and Control | TA329-348 Geocomputation and Urban Planning | TJ210.2-211.495 Design and Control of Intelligent Robotic Systems |
Fuzzy Sets and Systems -- Improved Fuzzy Clustering -- Fuzzy Functions Approach -- Modeling Uncertainty with Improved Fuzzy Functions -- Experiments -- Conclusions and Future Work.
The objective of this book is to present an uncertainty modeling approach using a new type of fuzzy system model via "Fuzzy Functions". Since most researchers on fuzzy systems are more familiar with the standard fuzzy rule bases and their inference system structures, many standard tools of fuzzy system modeling approaches are reviewed to demonstrate the novelty of the structurally different fuzzy functions, before we introduced the new methodologies. To make the discussions more accessible, no special fuzzy logic and system modeling knowledge is assumed. Therefore, the book itself may be a reference for some related methodologies to most researchers on fuzzy systems analyses. For those readers, who have knowledge of essential fuzzy theories, Chapter 1, 2 should be treated as a review material. Advanced readers ought to be able to read chapters 3, 4 and 5 directly, where proposed methods are presented. Chapter 6 demonstrates experiments conducted on various datasets.
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