CARNERERO PANDURO , Alfonso Daniel

El Control Predictivo basado en Modelo es, desde su aparición, una de las estrategias de control avanzado más populares y con mayor relevancia en la industria en la actualidad. Continuamente se publican nuevos resultados y avances, consiguiendo cada vez un mejor rendimiento del controlador o proporcionando garantías que antes no existían. Por...

This dissertation presents contributions mainly in three different fields: system identification, probabilistic forecasting and stochastic control. Thanks to the concept of dissimilarity and by defining an appropriate dissimilarity function, it is shown that a family of predictors can be obtained. First, a predictor to compute nominal...

Durante mucho tiempo, la computación paralela ha estado relegada a supercomputadores de grandes centros de investigación. Sin embargo, con la aparición de la arquitectura CUDA en las tarjetas gráficas de NVIDIA (las cuales no suponían una inversión desorbitada), empezó a estar disponible para un público mucho más amplio. Por otro lado,...

Stock price forecasting is a relevant and challenging problem that has attracted a lot of interest from engineers and scientists. In this paper we apply two techniques for stock price and price intervals forecasting. Both techniques, derived from previous works by the authors, are based on the use of local data extracted from a database. These...

The optimal execution of stock trades is a relevant and interesting problem as it is key in maximizing profits and reducing risks when investing in the stock market. In the case of large orders, the problem becomes even more complex as the impact of the order in the market has to be taken into account. The usual solution is to split large...

This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the future evolution of the system. The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs. A...

One of the main problems associated with advanced control strategies is their implementation on embedded and industrial platforms, especially when the target application requires real-time operation. Frequently, the dynamics of the system are totally or partially unknown, and data-driven methods are needed to learn an approximate model of the...