Mostrando 5 resultados de: 5
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2016 IEEE 55th Conference on Decision and Control, CDC 2016(1)
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Ciencias de la computación(2)
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Optimización matemática(3)
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Origen
scopus(5)
Characterizing the pbkp_redictive accuracy of dynamic mode decomposition for data-driven control
Conference ObjectAbstract: Dynamic mode decomposition (DMD) is a versatile approach that enables the construction of low-orderPalabras claves:control, DATA, Dynamic mode decomposition, error bounds, Low-order modelsAutores:Lu Q., Shin S., Víctor M. ZavalaFuentes:scopusBayesian optimization with reference models: A case study in MPC for HVAC central plants
ArticleAbstract: We present a framework for exploiting reference models in Bayesian optimization (BO). Our approach iPalabras claves:Bayesian optimization, HVAC Plants, MPC Tuning, Reference modelsAutores:González L.D., Kumar R., Lu Q., Víctor M. ZavalaFuentes:scopusAn economic model pbkp_redictive control framework for mechanical pulping processes
ArticleAbstract: We develop a multi-objective economic model pbkp_redictive control (m-econ MPC) framework to controlPalabras claves:Economic model pbkp_redictive control, Mechanical pulping process, Moving horizon estimation, stabilityAutores:Gopaluni R.B., Lu Q., Olson J.A., Tian H., Víctor M. ZavalaFuentes:scopusMultiobjective economic model pbkp_redictive control of mechanical pulping processes
Conference ObjectAbstract: We present a multiobjective economic model pbkp_redictive control (m-econ MPC) strategy to mitigatePalabras claves:Autores:Gopaluni R.B., Lu Q., Tian H., Víctor M. ZavalaFuentes:scopusImage-based model pbkp_redictive control via dynamic mode decomposition
ArticleAbstract: We present a data-driven model pbkp_redictive control (MPC) framework for systems with high state–spPalabras claves:Data-driven, Dynamic mode decomposition, Image data, Model Pbkp_redictive Control, Model reductionAutores:Lu Q., Víctor M. ZavalaFuentes:scopus