Fuzzy Membership Function Initial Values: Comparing Initialization Methods That Expedite Convergence

Fuzzy attributes are used to quantify imprecise data that model real world objects. To effectively use fuzzy attributes, a fuzzy membership function must be defined to provide the boundaries for the fuzzy data. The initialization of these membership function values should allow the data to converge to a stable membership value in the shortest time possible. The paper compares three initialization methods, Random, Midpoint and Random Proportional, to determine which method optimizes convergence. The comparison experiments suggest the use of the Random Proportional method.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year dc:date.available

2005

Author dc:creator

• Lee, Stephanie Scheibe

Contributors dc:contributor

• Dr. Lorraine M. Parker

• logic
• convergence
• initiliazation
• database
• fuzzy
• Computer Sciences
• Physical Sciences and Mathematics

Statement dc:rights

• © The Author

Identifier

https://scholarscompass.vcu.edu/etd/852

OAI identifier oai:identifier

oai:scholarscompass.vcu.edu:etd-1851