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This academic paper explores a new teaching approach to fuzzy logic system design, covering theory, practice, and research. Insights from experts and engineers are discussed, with examples of image warping presented. Various rules, functions, and operations are detailed along with the significance of literacy, computancy, motivation, attendance, search, artistic work, and grades in the learning process. The text references key works in fuzzy logic and emphasizes the importance of collaboration in scientific development.
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A New Approach to Teaching Fuzzy Logic System Design Emine Inelmen,Erol Inelmen, Ahmad Ibrahim Padova University,Padova, Italy Bogazici University,Istanbul, Turkey DeVry Institute of Technology,Toronto, Ontario, Canada
EXPERT ENGINEER USER
INTRODUCTION DISCUSSION CONCLUSION
THEORY PRACTICE RESEARCH
TEXTBOOKS CATALOGUES JOURNALS
Fig. 1. Examples of image warping (from the source to the target figure [15]
RULES FUNCTIONS OPERATIONS
'literacy‘'computancy‘'motivation‘'attendance‘'search‘'artistic‘'work‘'grade''literacy‘'computancy‘'motivation‘'attendance‘'search‘'artistic‘'work‘'grade'
Yen, J.Langari; R., Fuzzy Logic: Intelligence, Control, and Information. Upper Saddle River, Prentice Hall, N.J. (1999).
fuzzification defuzzification has aggregation universe implication turns to turns to can be has iak crip set element reduces by number inference iak set has operator name has two solves by forms uses has fuzzy set range can be Fuzzy Logic relation uses multi iak value fires has uses uses logic has smooth boundaries has has rules possibility T-norm define membership type hedge has T-conorm consequent can have membership function has modifier defines antecendent has term defines membership degree has linguistic variable 19.06.03 uses
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Acknowledgement The support given by Dr. Zenon J. Pudlowski is acknowledged. Science can only develop when organizations like UICEE create strong networks between researches in different fields and geographies.