Alberto Guillén

TL;DR: A new procedure to predict time series using paradigms such as: fuzzy systems, neural networks and evolutionary algorithms, so that the linear model can be identified automatically, without the need of human expert participation is presented.

TL;DR: This paper proposes a hybridization of intelligent techniques such as ANNs, fuzzy systems and evolutionary algorithms, so that the final hybrid ARIMA-ANN model could outperform the prediction accuracy of those models when used separately.

TL;DR: This paper presents the utility of two different methodologies, the TaSe fuzzy TSK model and the least-squares SVMs, to solve the problem of long term time series prediction using recursive prediction.

TL;DR: Two main approaches are proposed: the combination of features extracted from electroencephalogram (EEG) signal by different extraction methods, and the use of stacked sequential learning to incorporate predicted information from nearby sleep stages in the final classifier.

TL;DR: Path-coefficient analysis based on an ontogenetic model was used to study the relationships between tuber yield and yield components as influenced by cultivar and nitrogen fertilization, finding that compensatory mechanisms during the formation of the three yield components in the potato resulted stronger in the N experiments.