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Wyszukujesz frazę "Miczulski, W." wg kryterium: Autor


Wyświetlanie 1-3 z 3
Tytuł:
Influence of the GMDH neural network data preparation method on UTC(PL) correction prediction results
Autorzy:
Miczulski, W.
Sobolewski, Ł.
Powiązania:
https://bibliotekanauki.pl/articles/221698.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
GMDH neural network
national timescale
atomic clock
time series analysis
Opis:
The article presents results of the influence of the GMDH (Group Method of Data Handling) neural network input data preparation method on the results of predicting corrections for the Polish timescale UTC(PL). Prediction of corrections was carried out using two methods, time series analysis and regression. As appropriate to these methods, the input data was prepared based on two time series, ts1 and ts2. The implemented research concerned the designation of the prediction errors on certain days of the forecast and the influence of the quantity of data on the prediction error. The obtained results indicate that in the case of the GMDH neural network the best quality of forecasting for UTC(PL) can be obtained using the time-series analysis method. The prediction errors obtained did not exceed the value of š 8 ns, which confirms the possibility of maintaining the Polish timescale at a high level of compliance with the UTC.
Źródło:
Metrology and Measurement Systems; 2012, 19, 1; 123-132
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new elastic scheduling task model in the node of a control and measurement system
Autorzy:
Miczulski, W.
Powroźnik, P.
Powiązania:
https://bibliotekanauki.pl/articles/220884.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
task scheduling
elastic scheduling task model
heuristic algorithms
measurement and control systems
time deadline fulfilment
Opis:
The paper presents a new elastic scheduling task model which has been used in the uniprocessor node of a control measuring system. This model allows the selection of a new set of periods for the occurrence of tasks executed in the node of a system in the case when it is necessary to perform additional aperiodic tasks or there is a need to change the time parameters of existing tasks. Selection of periods is performed by heuristic algorithms. This paper presents the results of the experimental use of an elastic scheduling model with a GRASP heuristic algorithm.
Źródło:
Metrology and Measurement Systems; 2013, 20, 1; 87-98
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks
Autorzy:
Luzar, M.
Sobolewski, Ł.
Miczulski, W.
Korbicz, J.
Powiązania:
https://bibliotekanauki.pl/articles/202125.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural network
prediction methods
national timescale
atomic clock
Opis:
In this paper, the effectiveness of using Artificial Neural Networks (ANNs) for predicting the corrections of the Polish time scale UTC(PL) (Universal Coordinated Time) is presented. In particular, prediction results for the different types of neural networks, i.e., the MLP (MultiLayer Perceprton), the RBF (Radial Basis Function) and the GMDH (Group Method of Data Handling) are shown. The main advantages and disadvantages of using such types of neural networks are discussed. The prediction of corrections is performed using two methods: the time series analysis method and the regression method. The input data were prepared suitable for the above mentioned methods, based on two time series, ts1 and ts2. The designation of prediction errors for specified days and the influence of data quantity for the prediction error are considered. The paper consists of five sections. After Introduction, in Sec. 2, the theoretical background for different types of neural networks is presented. Section 3 shows data preparation for the appropriate type of neural network. The experimental results are presented in Sec. 4. Finally, Sec. 5 concludes the paper.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 3; 589-594
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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