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Lower Bounds for Approximation of Some Classes of Lebesgue Measurable Functions by Sigmoidal Neural Networks Read chapter Read first chapter

2009 | OriginalPaper | Chapter

Adaptative Resonance Theory Fuzzy Networks Parallel Computation Using CUDA

Authors : M. Martínez-Zarzuela, F. J. Díaz Pernas, A. Tejero de Pablos, M. Antón Rodríguez, J. F. Díez Higuera, D. Boto Giralda, D. González Ortega

Published in: Bio-Inspired Systems: Computational and Ambient Intelligence

Publisher: Springer Berlin Heidelberg

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Programming of Graphics Processing Units (GPUs) has evolved in a way they can be used to address and speed-up computation of algorithms exemplified by data-parallel models. In this paper parallelization of a Fuzzy ART algorithm is described and a detailed explanation of its implementation under CUDA is given. Experimental results show the algorithm runs up to 52 times faster on the GPU than on the CPU for testing and 18 times faster for training under specific conditions.

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previous chapter Convergence in an Adaptive Neural Network: The Influence of Noise Inputs Correlation
next chapter A Supervised Learning Method for Neural Networks Based on Sensitivity Analysis with Automatic Regularization
Metadata
Title
Adaptative Resonance Theory Fuzzy Networks Parallel Computation Using CUDA
Authors
M. Martínez-Zarzuela
F. J. Díaz Pernas
A. Tejero de Pablos
M. Antón Rodríguez
J. F. Díez Higuera
D. Boto Giralda
D. González Ortega
Copyright Year
2009
Publisher
Springer Berlin Heidelberg
Book
Bio-Inspired Systems: Computational and Ambient Intelligence

Print ISBN: 978-3-642-02477-1

Electronic ISBN: 978-3-642-02478-8

Copyright Year: 2009

DOI
https://doi.org/10.1007/978-3-642-02478-8_19

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