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dc.contributor.authorPérez Fernández, Javier
dc.contributor.authorCabrera-Carrillo, Juan Antonio 
dc.contributor.authorCastillo-Aguilar, Juan Jesus 
dc.date.accessioned2018-07-30T11:41:18Z
dc.date.available2018-07-30T11:41:18Z
dc.date.created2018
dc.date.issued2018-07-30
dc.identifier.urihttps://hdl.handle.net/10630/16382
dc.description.abstractA traction control system is designed and trained for different road conditions with co-evolutionary learning based on a genetic algorithm. Common solutions do not consider the variation and oscillation created in the transition between roads defining a control logic which is highly dependent on road accuracy and a speed estimator. To solve this problem, a co-evolutionary learning process is used. This procedure trains the control algorithm, a spiking neural network, on different roads and transitions looking for the worst-case scenario. We have developed a control algorithm with a good dynamic response to constant and changing roads. This control algorithm makes the system stable when the road estimation is delayed or unstable, solving a common flaw produced by sensor noise or computation delays.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlgoritmos genéticosen_US
dc.subject.otherSpiking Neural Networken_US
dc.subject.otherCo-Evolutionen_US
dc.titleA traction Control System based on Co-evolutionary Learning in Spiking Neural Networks (SNN)en_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroEscuela de Ingenierías Industrialesen_US
dc.relation.eventtitleAVEC18- 14th International Symposium on Avanced Vehicle Controlen_US
dc.relation.eventplaceBeijing (CHINA)en_US
dc.relation.eventdate16-20 Julio 2018en_US


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