{"id":{"repo_id":"vcu","oai_identifier":"oai:scholarscompass.vcu.edu:etd-1851"},"canonical_url":"https://search.dev.ndltd.org/etd/vcu/oai:scholarscompass.vcu.edu:etd-1851","repository":{"repo_id":"vcu","name":"Virginia Commonwealth University","base_url":"https://scholarscompass.vcu.edu/do/oai/"},"display":{"title":"Fuzzy Membership Function Initial Values: Comparing Initialization Methods That Expedite Convergence","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Lee, Stephanie Scheibe"],"institution":null,"degree_name":"Master of Science","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dr. Lorraine M. Parker"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-01-01T08:00:00Z","date_published":"2005-01-01T08:00:00Z","updated_at":"2026-07-24T05:54:30Z","subjects":["logic","convergence","initiliazation","database","fuzzy","Computer Sciences","Physical Sciences and Mathematics"],"languages":[],"rights":["© The Author"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarscompass.vcu.edu/etd/852"],"render_values":[{"text":"https://scholarscompass.vcu.edu/etd/852","href":"https://scholarscompass.vcu.edu/etd/852","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.25772/HKBB-M048","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Lorraine M. 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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."]},{"key":"dc:title","label":"Title","values":["Fuzzy Membership Function Initial Values: Comparing Initialization Methods That Expedite Convergence"]}]}],"canonical_facts":{"dc:contributor":["Dr. Lorraine M. Parker"],"dc:creator":["Lee, Stephanie Scheibe"],"dc:date.available":["2014-07-09T07:00:00Z"],"dc:description.abstract":["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."],"dc:identifier":["https://doi.org/10.25772/HKBB-M048","https://scholarscompass.vcu.edu/etd/852"],"dc:rights":["© The Author"],"dc:subject":["logic","convergence","initiliazation","database","fuzzy","Computer Sciences","Physical Sciences and Mathematics"],"dc:title":["Fuzzy Membership Function Initial Values: Comparing Initialization Methods That Expedite Convergence"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T05:54:30Z"}