Authors:
José A. Torres
;
Sergio Martinez
;
Francisco J. Martinez
and
Mercedes Peralta
Affiliation:
University of Almería, Spain
Keyword(s):
Neural Networds, Large Training Sets, SOM-RBF Mixed Model, Ensemble of Neural Networks, Environmental Applications.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
The paper presents a technique to partition and sort data in a large training set for building models of envi-ronmental function approximation using RBFs networks. This process allows us to make very accurate ap-proximations of the functions in a time fraction related to the RBF networks classic training proccess. Fur-thermore, this technique avoids problems of buffer overflow in the training algorithm execution. The results obtained proved similar accuracy to those obtained with a classical model in a time substantially less, opening, on the other hand, the way to the parallelization process using GPUs technology.