Wpływ doboru parametrów sieci neuronowej GMDH na wyniki prognozy poprawek dla krajowej skali czasu UTC(PL)

• Autorzy: Sobolewski Ł.

Warianty tytułu

EN

Influence of selection of GMDH neural network parameters on predicted corrections of the national time scale UTC(PL)

• Języki publikacji: PL

Abstrakty

PL

W pracy omówiono wyniki badań dotyczących prognozowania poprawek dla krajowej skali czasu UTC(PL), z wykorzystaniem sztucznych sieci neuronowych GMDH. Przedstawiono wyniki badań, których celem było sprawdzenie, jak dobór funkcji przejścia neuronu sieci GMDH oraz stosunku danych uczących do danych testujących wpływają na wynik prognozy. Opisano wyniki prognozowania poprawek otrzymane na podstawie przeprowadzonych badań, uzyskane na 15 dzień dla 28 kolejnych miesięcy, począwszy od stycznia 2008 roku (MJD 54479) do kwietnia 2010 roku (MJD 55299).

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 parameters, ie. the transfer function of the neuron and the ratio of training to test data 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 for predicting the corrections used in the GUM as well as 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) were prepared. They were the basis for determining the input to the GMDH neural network. The third section describes the basic idea and principle of operation of GMDH neural networks, which belong to the group of self-organizing networks. In the fourth section there is presented the method for predicting the corrections using GMDH neural networks and there are given the research results. There were carried out investigations whose aim was to examine the influence of the transfer function of the neuron and the ratio of training to test data on the prediction result. Based on those investigations the prediction of the corrections on the 15th day of 28 consecutive months was performed. The research show that the GMDH neural networks can be used for predicting the corrections for the national time scale UTC(PL). The obtained prediction errors are significantly smaller than those obtained from the analytical linear regression method used in the GUM. It is shown that a significant influence on obtaining small prediction errors has a proper selection of the GMDH neural network parameters.

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: 2012
• Tom: R. 58, nr 10
• Strony: 869--871
• Opis fizyczny: Bibliogr. 8 poz., rys., wzory

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] Panfilo G. and Tavella P.: Atomic clock prediction based on stochastic differential equations, Metrologia, No. 45, 2008, pp. 108-116.