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Comparison of hierarchical temporal memories and artificial neural networks under noisy data

Abstract: The ability of two different machine learning approaches to map non-linear problems from experimental data is evaluated under controlled experiments. A well-known machine learning algorithm (Artificial Neural Network) is compared against a new computing paradigm (Hierarchical Temporal Memory) under a controlled scenario. The chosen scenario is the detection of impacts in a cantilever beam under vibration instrumented with fiber Bragg gratings. The main characteristics of both of the machine learning approaches are analyzed while varying environmental parameters such as the number of sensing points and their location. From the achieved results some clues can be extracted regarding dealing with noisy or partial data using different machine learning approaches.

 Autoría: Rodriguez-Cobo L., Mirapeix J., Cobo A., Lopez-Higuera J.,

 Fuente: Journal of Intelligent Material Systems 2015, Vol. 26(10) 1243–1250

Editorial: SAGE Publications Ltd

 Año de publicación: 2015

Nº de páginas: 8

Tipo de publicación: Artículo de Revista

DOI: 10.1177/1045389X14538537

ISSN: 1045-389X,1530-8138

Proyecto español: TEC2010-20224-C02-02

Url de la publicación: http://dx.doi.org/10.1177/1045389X14538537