{"id":{"repo_id":"siu-theses","oai_identifier":"oai:opensiuc.lib.siu.edu:dissertations-1255"},"canonical_url":"https://search.dev.ndltd.org/etd/siu-theses/oai:opensiuc.lib.siu.edu:dissertations-1255","repository":{"repo_id":"siu-theses","name":"Southern Illinois University","base_url":"https://opensiuc.lib.siu.edu/do/oai/"},"display":{"title":"The Proteretic Hopfield Neural Network Analog to Digital Converter","abstract":"The Hopfield neuron with predictive hysteresis is proposed and the efficiency of employing these neurons in analog to digital conversion, via the Hopfield Neural Network, will be demonstrated. Traditional hysteresis is defined, generally, as the occurrence of a delayed effect when forces acting on an object are varied. It will be shown that predictive hysteresis, a type of reverse hysteresis, improves upon the speed of hysteretic and non hysteretic systems without compromising the accuracy.","abstract_html":"The Hopfield neuron with predictive hysteresis is proposed and the efficiency of employing these neurons in analog to digital conversion, via the Hopfield Neural Network, will be demonstrated. Traditional hysteresis is defined, generally, as the occurrence of a delayed effect when forces acting on an object are varied. It will be shown that predictive hysteresis, a type of reverse hysteresis, improves upon the speed of hysteretic and non hysteretic systems without compromising the accuracy.","abstract_has_math":false,"creators":["Calmese, Ife"],"institution":null,"degree_name":"Doctor of Philosophy","degree_level":"Campus Only Dissertation","degree_discipline":"Electrical and Computer Engineering","degree_department":null,"school":null,"contributors":["Sayeh, Mohammed"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008-01-01T08:00:00Z","date_published":"2008-01-01T08:00:00Z","updated_at":"2026-07-24T04:33:39Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://opensiuc.lib.siu.edu/dissertations/255","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sayeh, Mohammed"]},{"key":"dc:creator","label":"Author","values":["Calmese, Ife"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Campus Only Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://opensiuc.lib.siu.edu/dissertations/255"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The Hopfield neuron with predictive hysteresis is proposed and the efficiency of employing these neurons in analog to digital conversion, via the Hopfield Neural Network, will be demonstrated. Traditional hysteresis is defined, generally, as the occurrence of a delayed effect when forces acting on an object are varied. It will be shown that predictive hysteresis, a type of reverse hysteresis, improves upon the speed of hysteretic and non hysteretic systems without compromising the accuracy."]},{"key":"dc:title","label":"Title","values":["The Proteretic Hopfield Neural Network Analog to Digital Converter"]}]}],"canonical_facts":{"dc:contributor":["Sayeh, Mohammed"],"dc:creator":["Calmese, Ife"],"dc:description.abstract":["The Hopfield neuron with predictive hysteresis is proposed and the efficiency of employing these neurons in analog to digital conversion, via the Hopfield Neural Network, will be demonstrated. Traditional hysteresis is defined, generally, as the occurrence of a delayed effect when forces acting on an object are varied. It will be shown that predictive hysteresis, a type of reverse hysteresis, improves upon the speed of hysteretic and non hysteretic systems without compromising the accuracy."],"dc:identifier":["https://opensiuc.lib.siu.edu/dissertations/255"],"dc:title":["The Proteretic Hopfield Neural Network Analog to Digital Converter"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["Campus Only Dissertation"],"thesis:degree_name":["Doctor of Philosophy"]},"updated_at":"2026-07-24T04:33:39Z"}