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Your search keyword '"Pinzolas, Miguel"' showing total 3 results
Start Over Author "Pinzolas, Miguel" Topic computer science
3 results on '"Pinzolas, Miguel"'

1. Supervised dFasArt: A Neuro-fuzzy Dynamic Architecture for Maneuver Detection in Road Vehicle Collision Avoidance Support Systems.

Author

Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Rangan, C. Pandu, Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Mira, José, Álvarez, José R., Toledo, Rafael, Pinzolas, Miguel, and Cano-Izquierdo, Jose Manuel

Abstract

A supervised version of dFasArt, a neuronal architecture based method that employs dynamic activation functions determined by fuzzy sets is used for solving support of the problem of inter-vehicles collisions in roads. The dynamic character of dFasArt minimizes problems caused by noise in the sensors and provides stability on the predicted maneuvers. To test the proposed algorithm, several experiments with real data have been carried out, with good results. [ABSTRACT FROM AUTHOR]

Published
2007
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2. A Functional Simplification of the BCS/FCS Image Segmentation.

Author

Marques, Jorge S., Pérez de la Blanca, Nicolás, Pina, Pedro, Martínez, Pablo, Pinzolas, Miguel, López Coronado, Juan, and García, Daniel

Abstract

In this paper, a functional simplification of the BCS/FCS neurobiological model for image segmentation is presented. The inherent complexity of the BCS/FCS system is mainly due to the close modelling of the cortical mechanisms and to the high number of parameters involved. For functional applications, the proposed simplification retains both the biological concepts of the BCS/FCS and its performance, while greatly reducing the number of parameters and the execution time. [ABSTRACT FROM AUTHOR]

Published
2005
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3. dFasArt: Dynamic neural processing in FasArt model

Author

Cano-Izquierdo, Jose-Manuel, Almonacid, Miguel, Pinzolas, Miguel, and Ibarrola, Julio

Subjects
*ARTIFICIAL neural networks, *COMPUTER input design, *FUZZY systems, *INPUT-output analysis, *COMPUTER architecture, *COMPUTER science
Abstract

The temporal character of the input is, generally, not taken into account in the neural models. This paper presents an extension of the FasArt model focused on the treatment of temporal signals. FasArt model is proposed as an integration of the characteristic elements of the Fuzzy System Theory in an ART architecture. A duality between the activation concept and membership function is established. FasArt maintains the structure of the Fuzzy ARTMAP architecture, implying a static character since the dynamic response of the input is not considered. The proposed novel model, dynamic FasArt (dFasArt), uses dynamic equations for the processing stages of FasArt: activation, matching and learning. The new formulation of dFasArt includes time as another characteristic of the input. This allows the activation of the units to have a history-dependent character instead of being only a function of the last input value. Therefore, dFasArt model is robust to spurious values and noisy inputs. As experimental work, some cases have been used to check the robustness of dFasArt. A possible application has been proposed for the detection of variations in the system dynamics. [Copyright &y& Elsevier]

Published
2009
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