Next Article in Journal
Complexity and Vulnerability Analysis of the C. Elegans Gap Junction Connectome
Previous Article in Journal
Emergence of Distinct Spatial Patterns in Cellular Automata with Inertia: A Phase Transition-Like Behavior
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Entropy 2017, 19(3), 103; doi:10.3390/e19030103

Analysis of the Temporal Structure Evolution of Physical Systems with the Self-Organising Tree Algorithm (SOTA): Application for Validating Neural Network Systems on Adaptive Optics Data before On-Sky Implementation

1
Departamento de Explotación y Prospección de Minas, University of Oviedo, Independencia 13, 33004 Oviedo, Spain
2
Departamento de Física, Universidad de Oviedo, Calvo Sotelo s/n, 33007 Oviedo, Spain
3
Departamento de Administración de Empresas, Universidad de Oviedo, Avenida de El Cristo s/n, 33071 Oviedo, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 18 January 2017 / Revised: 24 February 2017 / Accepted: 5 March 2017 / Published: 7 March 2017
(This article belongs to the Section Information Theory)
View Full-Text   |   Download PDF [1135 KB, uploaded 8 March 2017]   |  

Abstract

Adaptive optics reconstructors are needed to remove the effects of atmospheric distortion in optical systems of large telescopes. The use of reconstructors based on neural networks has been proved successful in recent times. Some of their properties require a specific characterization. A procedure, based in time series clustering algorithms, is presented to characterize the relationship between temporal structure of inputs and outputs, through analyzing the data provided by the system. This procedure is used to compare the performance of a reconstructor based in Artificial Neural Networks, with one that shows promising results, but is still in development, in order to corroborate its suitability previously to its implementation in real applications. Also, this procedure could be applied with other physical systems that also have evolution in time. View Full-Text
Keywords: Artificial Neural Networks; time series clustering; adaptive optics Artificial Neural Networks; time series clustering; adaptive optics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Suárez Gómez, S.L.; Santos Rodríguez, J.D.; Iglesias Rodríguez, F.J.; de Cos Juez, F.J. Analysis of the Temporal Structure Evolution of Physical Systems with the Self-Organising Tree Algorithm (SOTA): Application for Validating Neural Network Systems on Adaptive Optics Data before On-Sky Implementation. Entropy 2017, 19, 103.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top