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Description
  • Simple neural networks with switching units are capable to predict seasonal time series with results comparable to common stochastic methods. This paper presents enhanced model of neural network with switching units with aim to improve the forecasting performance of non-stationary time series. The presented model of neural network network is build of neurons with feedback and continuous activation function and it has a two level topology. The paper further describes the application of genetic algorithms to the optimization of the first level of topology. Finally, the performance of the proposed model was tested on the time series of currency in circulation and two artificial seasonal stochastic processes. Experimental results confirm that the new model outperforms the basic one as well as common stochastic methods.
  • Simple neural networks with switching units are capable to predict seasonal time series with results comparable to common stochastic methods. This paper presents enhanced model of neural network with switching units with aim to improve the forecasting performance of non-stationary time series. The presented model of neural network network is build of neurons with feedback and continuous activation function and it has a two level topology. The paper further describes the application of genetic algorithms to the optimization of the first level of topology. Finally, the performance of the proposed model was tested on the time series of currency in circulation and two artificial seasonal stochastic processes. Experimental results confirm that the new model outperforms the basic one as well as common stochastic methods. (en)
Title
  • Neural Network with Cooperative Switching Units with Application to Time Series Forecasting
  • Neural Network with Cooperative Switching Units with Application to Time Series Forecasting (en)
skos:prefLabel
  • Neural Network with Cooperative Switching Units with Application to Time Series Forecasting
  • Neural Network with Cooperative Switching Units with Application to Time Series Forecasting (en)
skos:notation
  • RIV/67985807:_____/09:00323370!RIV10-MSM-67985807
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0567), Z(AV0Z10300504)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 329225
http://linked.open...ai/riv/idVysledku
  • RIV/67985807:_____/09:00323370
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • neural networks; time series forecasting; genetic optimization (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [E35DCF4D7538]
http://linked.open...v/mistoKonaniAkce
  • Los Angeles
http://linked.open...i/riv/mistoVydani
  • Los Alamitos
http://linked.open...i/riv/nazevZdroje
  • World Congress on Computer Science and Information Engineering
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Hakl, František
  • Hlaváček, M.
  • Kalous, R.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society
https://schema.org/isbn
  • 978-0-7695-3507-4
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