Please use this identifier to cite or link to this item: https://rinacional.tecnm.mx/jspui/handle/TecNM/4481
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dc.contributor.advisorALVARADO GRANADINO, JESUS ARTURO%458243-
dc.contributor.advisorCAMACHO RIOS, ALBERTO%16280-
dc.contributor.authorGARCIA REYES, CARLOS RICARDO TONATIUH-
dc.creatorGARCIA REYES, CARLOS RICARDO TONATIUH%950803-
dc.date.accessioned2022-09-12T19:25:36Z-
dc.date.available2022-09-12T19:25:36Z-
dc.date.issued2021-09-10-
dc.identifier.urihttps://rinacional.tecnm.mx/jspui/handle/TecNM/4481-
dc.descriptionThis document contains the complete research for a Reinforcement (RL) Learning Artificial Intelligence (IA) for the automatic control of a PEDE LEC power assistance module. The RL IA will use trial and error methods to determine the best available assistance for the PEDELEC rider using as reference the Torque produced at the crankshaft and the rider’s heartbeat at one second intervals. This data w ill be transformed into an RL Environment where an agent will learn from no previous experience how to increase or decrease the assistance level to maintain the rider’s heartbeat at a comfortable level. The implementation of the RL will use the principles of TD(0) control and SARSA Prediction to achieve the goal.es_MX
dc.language.isoenges_MX
dc.publisherTecnológico Nacional de Méxicoes_MX
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0es_MX
dc.subjectinfo:eu-repo/classification/cti/7es_MX
dc.titleREINFORCEMENT LEARNING MODULE FOR PERSONALIZED ASSISTED PEDALING IN HUMAN-ELECTRIC HYBRID VEHICLES (PEDELEC)es_MX
dc.typeinfo:eu-repo/semantics/masterThesises_MX
dc.contributor.directorDE LA GARZA GUTIERREZ, HERNAN%100484-
dc.contributor.directorSANDOVAL RODRIGUEZ, RAFAEL%123608-
dc.rights.accessinfo:eu-repo/semantics/openAccesses_MX
dc.publisher.tecnmInstituto Tecnológico de Chihuahua IIes_MX
Appears in Collections:TESIS MAESTRIA EN SISTEMAS COMPUTACIONALES

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