RODRÍGUEZ RAMÍREZ , Daniel

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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...

This article presents an extension to the nonlinear model predictive control (MPC) for tracking scheme able to guarantee convergence even in cases of nonconvex output admissible sets. This is achieved by incorporating a convexifying homeomorphism in the optimization problem, allowing it to be solved in the convex space. A novel class of...

In this paper we present a combinatorial nonlinear technical indicator approach for the identification of appropriate combinations of stock technical indicators as inputs in non-linear models. This approach is illustrated with the example of Chinese stock indexes and 35 different stock technical indicators using neural networks as the chosen...

En este trabajo se demuestra como la ley de control Min Max MPC con una función de coste cuadrática y de horizonte finito resulta ser una ley de control lineal a trozos, cuandos e tienen en cuenta incertidumbres aditivas acotadas. Los resultados se ilustran con un ejemplo de simulación.

Narrow markets are typically considered those that due to limited liquidity or peculiarities in its investor base, such as a particularly high concentration of retail investors, make the stock market less e cient and arguably less predictable. We show in this article that neural networks, applied to narrow markets, can provide relatively...

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...