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.
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 ± 8 ns, which confirms the possibility of maintaining the Polish timescale at a high level of compliance with the UTC.