Increasing the share of correct clustering of characteristic signal with random losses in self-organizing maps

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.160670

Keywords:

self-organizing map, SOM, ESOINN, Kohonen neural networks, signal with losses, losses in a time series, classification in terms of the characteristic signal

Abstract

Analysis of methods for optimizing algorithms of functioning of the Kohonen neural networks, self-organizing maps (SOM), in terms of training speed and percentage of correct clustering was made. Effective optimization of self-organizing maps was determined by the second criterion, the enhanced self-organizing incremental neural network (ESOINN). It was established that in the case of incomplete input signal, that is the signal with losses at unknown time points, the share of correct clustering is unacceptably low with any SOM algorithms, both basic and optimized.

The incomplete signal was represented as the input vector of the neural network, the values of which are represented by a single array, that is without taking into consideration conformity of the moments of losses to the current values and without the possibility of determining these moments. A method for determining conformance of the incomplete input vector to the input layer of neurons to increase percentage of correct recognition was programmed and proposed. The method is based on finding the minimum distance between the current input vector and the vector of weights of each neuron. To reduce operating time of the algorithm, it was proposed to operate not with individual values of the input signal but their indivisible parts and the corresponding groups of input neurons. The proposed method was implemented for the SOM and ESOINN. To prove effectiveness of implementation of the basic algorithm of the SOM, its comparison with existing counterparts of other developers was made.

A mathematical model was developed for formation of examples of complete signals of a training sample on the basis of reference curves of the second order and a training sample was generated. In accordance with the training sample, training of all neural networks implemented with and without the proposed method was made. A diagram of simulation of losses was developed and test samples were generated for computational experiments with incomplete signals.

On the basis of experiments, efficiency of the proposed method for classification in terms of incomplete input signal on the basis of self-organizing maps was proved both for implementations of the basic algorithm of SOM and ESOINN

Author Biographies

Svitlana Shapovalova, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Peremohy ave., 37, Kyiv, Ukraine, 03056

PhD, Associate Professor

Department of Automation of Designing of Energy Processes and Systems

 

Yurii Moskalenko, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Peremohy ave., 37, Kyiv, Ukraine, 03056

Рostgraduate student

Department of Automation of Designing of Energy Processes and Systems

References

  1. Passoni, L. I., Dai Pra, A. I., Meschino, G. J., Guzman, M., Weber, C., Rabal, H., Trivi, M. (2014). Unsupervised learning segmentation for dynamic speckle activity images. arXiv. Available at: https://arxiv.org/abs/1408.3818
  2. Fustes, D., Manteiga, M., Dafonte, C., Arcay, B., Ulla, A., Smith, K. et. al. (2013). An approach to the analysis of SDSS spectroscopic outliers based on self-organizing maps. Astronomy & Astrophysics, 559, A7. doi: https://doi.org/10.1051/0004-6361/201321445
  3. Meusinger, H., Brünecke, J., Schalldach, P., in der Au, A. (2017). A large sample of Kohonen selected E+A (post-starburst) galaxies from the Sloan Digital Sky Survey. Astronomy & Astrophysics, 597, A134. doi: https://doi.org/10.1051/0004-6361/201629139
  4. Fraccalvieri, D., Bonati, L., Stella, F. (2013). Self Organizing Maps to efficiently cluster and functionally interpret protein conformational ensembles. Electronic Proceedings in Theoretical Computer Science, 130, 83–86. doi: https://doi.org/10.4204/eptcs.130.13
  5. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43 (1), 59–69. doi: https://doi.org/10.1007/bf00337288
  6. Su, M.-C., Liu, T.-K., Chang, H.-T. (2002). Improving the self-organizing feature map algorithm using an efficient initialization scheme. Tamkang Journal of Science and Engineering, 5 (1), 35–48.
  7. Shapovalova, S. I., Sharaievskyi, H. I. (2007). Kompiuterne modeliuvannia karty samoorhanizatsiyi dlia rozviazannia zadachi rozpiznavannia syhnaliv. Visnyk natsionalnoho universytetu “Lvivska politekhnika”, 574, 75–80.
  8. Su, M.-C., Chang, H.-T. (1998). Genetic-algorithms-based approach to self-organizing feature map and its application in cluster analysis. 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98CH36227). doi: https://doi.org/10.1109/ijcnn.1998.682372
  9. El Golli, A. (2005). Speeding up the self organizing map for dissimilarity data. Proceedings of International Symposium on Applied Stochastic Models and Data Analysis. Brest, 709–713.
  10. Conan-Guez, B., Rossi, F., El Golli, A. (2006). Fast algorithm and implementation of dissimilarity self-organizing maps. Neural Networks, 19 (6-7), 855–863. doi: https://doi.org/10.1016/j.neunet.2006.05.002
  11. Cuadros-Vargas, E., Romero, R. F., Obermayer, K. (2003). Speeding up algorithms of SOM family for large and high dimensional databases. In Workshop on Self Organizing Maps. Kitakyushu.
  12. Fritzke, B. (1994). Growing cell structures – A self-organizing network for unsupervised and supervised learning. Neural Networks, 7 (9), 1441–1460. doi: https://doi.org/10.1016/0893-6080(94)90091-4
  13. Cao, M., Li, A., Fang, Q., Kaufmann, E., Kröger, B. J. (2014). Interconnected growing self-organizing maps for auditory and semantic acquisition modeling. Frontiers in Psychology, 5. doi: https://doi.org/10.3389/fpsyg.2014.00236
  14. Cao, M., Li, A., Fang, Q., Kroger, B. J. (2013). Growing self-organizing map approach for semantic acquisition modeling. 2013 IEEE 4th International Conference on Cognitive Infocommunications (CogInfoCom). doi: https://doi.org/10.1109/coginfocom.2013.6719269
  15. Furao, S., Hasegawa, O. (2006). An incremental network for on-line unsupervised classification and topology learning. Neural Networks, 19 (1), 90–106. doi: https://doi.org/10.1016/j.neunet.2005.04.006
  16. Furao, S., Ogura, T., Hasegawa, O. (2007). An enhanced self-organizing incremental neural network for online unsupervised learning. Neural Networks, 20 (8), 893–903. doi: https://doi.org/10.1016/j.neunet.2007.07.008
  17. Algoritm Uluchshennoy Samoorganizuyushcheysya Rastushchey Neyronnoy Seti (ESOINN). Available at: https://habr.com/post/206116
  18. An enhanced self-organizing incremental neural network for online unsupervised learning. Available at: https://github.com/BelBES/ESOINN
  19. Encog Machine Learning Framework. Available at: https://github.com/encog/encog-java-core
  20. Neuroph – Java Neural Network Platform Neuroph. Available at: https://github.com/neuroph/neuroph
  21. Self-Organizing Incremental Neural Network. Available at: https://github.com/fukatani/soinn
  22. Growing Self-Organizing Map. Available at: https://github.com/philippludwig/pygsom

Downloads

Published

2019-03-25

How to Cite

Shapovalova, S., & Moskalenko, Y. (2019). Increasing the share of correct clustering of characteristic signal with random losses in self-organizing maps. Eastern-European Journal of Enterprise Technologies, 2(4 (98), 13–21. https://doi.org/10.15587/1729-4061.2019.160670

Issue

Vol. 2 No. 4 (98) (2019): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2019 Svitlana Shapovalova, Yurii Moskalenko

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.