Porównanie metod prognozowania zastosowanych w sieci neuronowej GMDH przeznaczonej do wyznaczania prognozy poprawek dla krajowej skali czasu UTC(PL)

• Autorzy: Sobolewski Ł.

Warianty tytułu

EN

Comparison of predicting methods used in GMDH neural network for determining the correction prediction for the national timescale UTC(PL)

• Języki publikacji: PL

Abstrakty

PL

W pracy zostały porównane metody prognozowania poprawek dla krajowej skali czasu UTC(PL). Badania dotyczące prognozowania poprawek prowadzono w oparciu o sieci neuronowe GMDH dwoma metodami, metodą analizy szeregów czasowych i metodą regresji. Prognozowanie poprawek zostało wykonane na 15 dzień miesiąca dla 20 kolejnych miesięcy. Otrzymane wyniki prognoz przy użyciu sieci neuronowej GMDH zestawione z wynikami prognoz otrzymanymi przez Główny Urząd Miar z zastosowaniem metody regresji liniowej pokazują, że lepszą metodą prognozowania poprawek dla krajowej skali czasu okazała się metoda analizy szeregów czasowych.

EN

The paper discusses the results of predicting the corrections for the national time scale UTC(PL), using GMDH neural networks. The aim of the research was to examine the influence of the GMDH neural network prediction methods on the prediction result. The first section describes the national time scale UTC(PL) and presents the problem of maintaining the best compatibility of the UTC(PL) with UTC. It also presents the method of predicting the corrections used in the Central Office of Measures (GUM), and a new method for predicting the corrections for the UTC(PL) based on GMDH neural network. The second section shows how the input data for the GMDH neural network was prepared. Based on historical measurement data from the cesium atomic clock Cs2 and corrections of the UTC(PL) relative to UTC, two time series (sc1 and sc2) which were the basis for determining the input to GMDH neural network were prepared. The third section describes the predicting methods used in the GMDH neural network and a training data for both methods. The fourth section focuses on the method of predicting the corrections using GMDH neural networks, and contains the research results. The research on predicting the corrections were carried out using two methods, the time series analysis and the regression method. Prediction of the corrections was made on the 15th day of month for 20 consecutive months. The prediction results using the GMDH neural network were compared with the results received by the GUM with use of the linear regression method. The research show that the GMDH neural networks can be used to predict the corrections for the national time scale UTC(PL). A better method of predicting the corrections for the national time scale proved to be the method of time series analysis. The results were better than the prediction results obtained in the GUM for both time series sc1 and sc2. In the case of using the regression method only for times series sc1, the obtained results were better than those obtained in the GUM.

Słowa kluczowe

PL

sieci neuronowe GMDH; polska skala czasu UTC(PL); zegar atomowy

EN

GMDH neural network; national timescale UTC(PL); atomic clock

• Wydawca: Wydawnictwo PAK
• Czasopismo: Pomiary Automatyka Kontrola
• Rocznik: 2013
• Tom: R. 59, nr 1
• Strony: 23--25
• Opis fizyczny: Bibliogr. 9 poz., rys., schem., tab.

Twórcy

autor

Sobolewski Ł.

• Uniwersytet Zielonogórski, Instytut Metrologii Elektrycznej, ul. Podgórna 50, 65-246 Zielona Góra

Bibliografia

• [1] Bernier L. G.: Use of the Allan Deviation and Linear Prediction for the Determination of the Uncertainty on Time Calibrations Against Predicted Timescales, IEEE Transactions on Instrumentation and Measurement, Vol.52, No. 2, 2003, pp. 483-486.
• [2] BIPM Annual Report on Time Activities, Vol. 6, Sevres, BIPM 2011.
• [3] Czubla A., Konopka J., Nawrocki J.: Realization of atomic SI second definition in context UTC(PL) and TA(PL); Metrology and Measurement Systems, No. 2, 2006, pp. 149-159.
• [4] Davis J. A., Shemar S. L., Whibberley P. B., A Kalman filter UTC(k) prediction and steering algorithm, NMS Physical Metrology Programme, United Kingdom.
• [5] Duch W., Korbicz J., Rutkowski L., Tadeusiewicz R.: Biocybernetyka i inżynieria biomedyczna - Sieci neuronowe, Akademicka oficyna Wydawnicza EXIT, Warszawa 2000.
• [6] Masters T.: Practical neural networks recipes in C++, Academic Press, Inc., 1993.
• [7] Miczulski W., Cepowski M.: Influence of type of neural network and selection of data pre-processing method on UTC-UTC(PL) prediction result; The Measurements, Automation and Monitoring, No. 11, 2010, pp. 1330-1332.
• [8] Miczulski W., Sobolewski Ł.: Influence of the GMDH neural network data preparation method on UTC(PL) correction prediction results, Metrology and Measurement Systems, Vol. XIX, No. 1, 2012, pp. 123-132.
• [9] Panfilo G. and Tavella P.: Atomic clock prediction based on stochastic differential equations, Metrologia, No. 45, 2008, pp. 108-116.