A recurrent neural network for real time electrical microgrid prototype optimization

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dc.contributor.authorLoza-López, Martín
dc.contributor.authorRuiz-Cruz, Riemann
dc.contributor.authorLoukianov, Alexander
dc.contributor.authorSánchez-Torres, Juan D.
dc.contributor.authorSánchez-Camperos, Edgar
dc.date.accessioned2016-04-26T15:05:05Z
dc.date.available2016-04-26T15:05:05Z
dc.date.issued2014-07-06
dc.descriptionThe aim of this paper is to present a new class of recurrent neural networks, which solve linear programming. It is considered as a sliding mode control problem, where the network structure is based on the Karush-Kuhn-Tucker (KKT) optimality conditions, and the KKT multipliers are the control inputs to be implemented with fixed time stabilizing terms, instead of common used activation functions. Thus, the main feature of the proposed network is its fixed convergence time to the solution, which means, there it is a time independent to the initial conditions in which the network converges to the optimization solution. The applicability of the proposed scheme is tested on real-time optimization of an electrical microgrid prototype.es
dc.description.sponsorshipConsejo Nacional de Ciencia y Tecnologíaes
dc.identifier.citationSánchez-Torres, J.D.; Loza-López, M.J.; Ruiz-Cruz, R.; Sánchez, E.N.; Loukianov, A.G., "A recurrent neural network for real time electrical microgrid prototype optimization,"Neural Networks (IJCNN), 2014 International Joint Conference on, Beijing, China, 6-11 July 2014, pp.2794,2799.es
dc.identifier.isbn978-1-4799-6627-1
dc.identifier.urihttp://hdl.handle.net/11117/3330
dc.language.isoenges
dc.publisherInstitute of Electrical and Electronics Engineerses
dc.relation.ispartofseries2014 International Joint Conference on Neural Networks (IJCNN);
dc.rights.urihttp://quijote.biblio.iteso.mx/licencias/CC-BY-NC-2.5-MX.pdfes
dc.subjectMicrogridses
dc.subjectFixed Time Stabilityes
dc.subjectRecurrent Neural Networkses
dc.subjectLinear Programminges
dc.titleA recurrent neural network for real time electrical microgrid prototype optimizationes
dc.typeinfo:eu-repo/semantics/conferencePaperes
rei.peerreviewedYeses
rei.revisor2014 International Joint Conference on Neural Networks (IJCNN)

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