Agile online-trained neural network models by using Robust Fixed Point Transformations

Térez A. Várkonyi, Vincenzo Piuri, József K. Tar, Annamaria R. Várkonyi-Kóczy, Imre J. Rudas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Nowadays, in the field of information processing, neural networks (NNs) are very used, because they can learn adaptively how to behave in a desired way. In the field of adaptive control, NNs are the most beneficial when the system to be controlled is not known in advance, because the system can be modeled by NNs to predict its behavior. In this case, it is very important how accurate the estimation of the NN is, since if the approximation is too rough then extra calculations are needed to get better results. One of the possibilities for improving the NN model is on-line learning during the control process, but this option requires high computational time. Another possibility is the application of Robust Fixed Point Transformations (RFPT), since though, it can only guarantee local stability, RFPT has been developed to reduce the inaccuracy caused by modeling errors and it does not need high computational time. In this paper, a new combination of the two methods the on-line trained neural networks and RFPT is proposed to decrease the computational burden caused by the on-line adaptation and keep the results close to optimum.

Original languageEnglish
Title of host publication2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings
PublisherIEEE Computer Society
Pages48-53
Number of pages6
ISBN (Print)9781467345439
DOIs
Publication statusPublished - Jan 1 2013
Event2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Funchal, Madeira, Portugal
Duration: Sep 16 2013Sep 18 2013

Publication series

Name2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings

Other

Other2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013
CountryPortugal
CityFunchal, Madeira
Period9/16/139/18/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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  • Cite this

    Várkonyi, T. A., Piuri, V., Tar, J. K., Várkonyi-Kóczy, A. R., & Rudas, I. J. (2013). Agile online-trained neural network models by using Robust Fixed Point Transformations. In 2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings (pp. 48-53). [6657481] (2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings). IEEE Computer Society. https://doi.org/10.1109/WISP.2013.6657481