A
Alberto Prieto
Researcher at University of Granada
Publications - 248
Citations - 4450
Alberto Prieto is an academic researcher from University of Granada. The author has contributed to research in topics: Artificial neural network & Fuzzy logic. The author has an hindex of 34, co-authored 248 publications receiving 4285 citations. Previous affiliations of Alberto Prieto include Royal Institute of Technology & Cisco Systems, Inc..
Papers
More filters
Journal ArticleDOI
Recursive prediction for long term time series forecasting using advanced models
Luis Javier Herrera,Héctor Pomares,Ignacio Rojas,Alberto Guillén,Alberto Prieto,Olga Valenzuela +5 more
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.
Journal ArticleDOI
TaSe, a Taylor series-based fuzzy system model that combines interpretability and accuracy
TL;DR: This paper presents a novel approach that extends the work by Bikdash in order to obtain an interpretable and accurate model for function approximation from a set of I/O data samples, which make use of the Taylor Series Expansion of a function around a point to approximate the function using a low number of rules.
Journal ArticleDOI
Structure identification in complete rule-based fuzzy systems
TL;DR: This paper presents a reliable method to obtain the structure of a complete rule-based fuzzy system for a specific approximation accuracy of the training data and decides which input variables must be taken into account in the fuzzy system.
Journal ArticleDOI
Separation of sources: a geometry-based procedure for reconstruction of n-valued signals
TL;DR: A new method is proposed for the separation of mixed digital sources, based on geometrical considerations, which is applied to the separated of binary and n-valued sources.
Journal ArticleDOI
Improved RAN sequential prediction using orthogonal techniques
TL;DR: It is claimed that the same techniques can be applied to the pruning problem, and thus they are a useful tool for compaction of information.