Abstract
The aim of the article is to use Adaptive Resonance Theory (ART1) for Big Data Filtering. ART1 is used for preprocessing of the training set. This allows finding typical patterns in the full training set and thus covering the whole space of solutions. The neural network adapted by a reduced training set has a greater ability of generalization. The work also discusses the influence of vigilance parameter settings for filtering the training set. The proposed method Big Data Filtering through Adaptive Resonance Theory is experimentally verified to control the behavior of an autonomous robot in an unknown environment. All obtained results are evaluated in the conclusion.
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Acknowledgments
The research described here has been financially supported by University of Ostrava grant SGS14/PřF/2016. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the sponsors.
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Barton, A., Volna, E., Kotyrba, M. (2017). Big Data Filtering Through Adaptive Resonance Theory. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_37
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DOI: https://doi.org/10.1007/978-3-319-54430-4_37
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