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A traction Control System based on Co-evolutionary Learning in Spiking Neural Networks (SNN)
dc.contributor.author | Pérez Fernández, Javier | |
dc.contributor.author | Cabrera-Carrillo, Juan Antonio | |
dc.contributor.author | Castillo-Aguilar, Juan Jesús | |
dc.date.accessioned | 2018-07-30T11:41:18Z | |
dc.date.available | 2018-07-30T11:41:18Z | |
dc.date.created | 2018 | |
dc.date.issued | 2018-07-30 | |
dc.identifier.uri | https://hdl.handle.net/10630/16382 | |
dc.description.abstract | A 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.sponsorship | Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. | en_US |
dc.language.iso | eng | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Algoritmos genéticos | en_US |
dc.subject.other | Spiking Neural Network | en_US |
dc.subject.other | Co-Evolution | en_US |
dc.title | A traction Control System based on Co-evolutionary Learning in Spiking Neural Networks (SNN) | en_US |
dc.type | info:eu-repo/semantics/conferenceObject | en_US |
dc.centro | Escuela de Ingenierías Industriales | en_US |
dc.relation.eventtitle | AVEC18- 14th International Symposium on Avanced Vehicle Control | en_US |
dc.relation.eventplace | Beijing (CHINA) | en_US |
dc.relation.eventdate | 16-20 Julio 2018 | en_US |