1 / 19

III. FUZZY LOGIC – Lecture 3

III. FUZZY LOGIC – Lecture 3. OBJECTIVES 1. To define the basic notions of fuzzy logic 2. To introduce the logical operations and relations on fuzzy sets 3. To learn how to obtain results of fuzzy logical operations 4. To apply what we learn to GIS.

avi
Download Presentation

III. FUZZY LOGIC – Lecture 3

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. III. FUZZY LOGIC – Lecture 3 OBJECTIVES 1. To define the basic notions of fuzzy logic 2. To introduce the logical operations and relations on fuzzy sets 3. To learn how to obtain results of fuzzy logical operations 4. To apply what we learn to GIS III. FUZZY LOGIC: Math Clinic Fall 2003

  2. OUTLINEIII. FUZZY LOGIC A. Introduction B. Inputs to fuzzy logic systems - fuzzification C. Fuzzy propositions D. Fuzzy hedges E. Computing the results of a fuzzy proposition given an input F. The resulting action III. FUZZY LOGIC: Math Clinic Fall 2003

  3. A. Introduction (figure from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  4. Introduction Steps (Earl Cox based on previous slide): 1. Input – vocabulary, fuzzification (creating fuzzy sets) 2. Fuzzy propositions – IF X is Y THEN Z (or Z is A) … there are four types of propositions 3. Hedges – very, extremely, somewhat, more, less 4. Combination and evaluation – computation of the results given the inputs 5. Action - defuzzification III. FUZZY LOGIC: Math Clinic Fall 2003

  5. Input – vocabulary, fuzzification (creating a fuzzy set) by using our previous methods of frequency, combination, experts/surveys (figure from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  6. Input (figure from Klir&Yuan) III. FUZZY LOGIC: Math Clinic Fall 2003

  7. Fuzzy Propositions – types 1 and 2 GENERAL FORMS 1. Unconditional and unqualified proposition: Q is P Example: Temperature(Q) is high(P) 2. Unconditional and qualified proposition: proposition(Q is P) is R Example: That Coimbra and Catania are beautiful is very true. III. FUZZY LOGIC: Math Clinic Fall 2003

  8. Fuzzy Proposition – type 1 and 2 (from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  9. Fuzzy Propositions – type 1 and 2 (from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  10. Fuzzy Propositions – type 3 3. Conditional and unqualified proposition: IF Q is P THEN R is S Example: If Robert is tall, then clothes are large. If car is slow, then gear is low. III. FUZZY LOGIC: Math Clinic Fall 2003

  11. Fuzzy Propositions – type 4 4. Conditional and qualified proposition: IF Q is P THEN R is S is T {proposition(IF Q is P THEN R is S )} is T III. FUZZY LOGIC: Math Clinic Fall 2003

  12. Fuzzy Hedges (from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  13. Fuzzy Hedges (from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  14. Illustrations of Fuzzy Propositions – Composition/Evaluation (from Klir&Yuan) III. FUZZY LOGIC: Math Clinic Fall 2003

  15. Illustrations of Fuzzy Propositions – Composition/Evaluation (Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  16. Illustrations of Fuzzy Propositions – Composition/Evaluation (from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  17. Illustrations of Fuzzy Propositions Decomposition – Defuzzification/Action (from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  18. Defuzzification (from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

  19. Defuzzification (from Earl Cox) III. FUZZY LOGIC: Math Clinic Fall 2003

More Related