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PL
W artykule przedstawiono budowę modelu podsystemu optymalizacji formuł algebry algorytmów metodą wprowadzenia dodatkowego warunku. Podsystem opisany został w postaci formuły algebry algorytmów. Przeprowadzono dekompozycje podsystemu na unitermy funkcyjne i zmienne. Opisano modele unitermów funkcyjnych i przedstawiono fragment implementacji podsystemu w języku C#.
EN
The subsystem model designed for algorithm algebra formula optimization by the introducing the additional condition there is described in the paper. The theory description and the example of an application are shown in the section 2. Next the subsystem model decomposition into variables and functional uniterms, by the introducing the additional condition is realized. Variables are used for the storage of data needed for algorithm formula transformations. The construction of functional algorithms is described using algorithm algebra expressions. Functional algorithms perform the following tasks: initiate variables, set and release elements of the table retaining engaged conditions, and choose first free condition. The subsystem model contains functional uniterms used for the checking of the ability to transformation, as well as perform the optimization itself. The model allows to introduce the additional condition by two ways, automatically or manually. In automatic mode the system itself set variables, while in manual mode the user chooses conditions by the keyboard. The implementation of the subsystem model performing the optimization by the introducing the additional condition is shown in C # program language. In conclusions some advantages of the using of the algorithm formula optimization computer system are pointed out. Sources bounded with algorithm algebra are given in the bibliography.
2
Content available Model systemu do przekształceń formuł algorytmów
PL
W artykule przedstawiono budowę złożonego komputerowego systemu do przekształceń formuł algorytmów przy wykorzystaniu właściwości operacji algebry algorytmów. Budowany model systemu nazwano komputerowym systemem optymalizacji (KSO) i dokonano jego dekompozycji na dwóch poziomach, poziomie systemu i podsystemu. Na poziomie systemu w logiczny sposób wyodrębniono podsystemy pełniące oraz wspomagające zadania optymalizacji formuł algorytmów, a na poziomie podsystemu utworzono unitermy zmienne i funkcyjne. Dla opisania modelu systemu wykorzystano algebrę algorytmów. Fragment modelu systemu zaimplementowano w języku C#.
EN
The model of algorithm algebra formulae optimization system is presented in the paper. The decomposition of the system into subsystems is performed using algebra algorithm expressions, and is described in terms of the expressions. The following algorithm algebra operation optimization subsystem models are formed: sequencing, elimination, parallelization, reversing, cycle operations, basic subsystem, subsystem introducing additional condition, subsystem rendering data accessible, and the one generating indices for xml code of algorithm formula. The basic subsystem model and the rendering data accessible one in the 3rd section there are described. Algorithm algebra expressions there are used consistently. Models are composed of variables and functional uniterms, which are also used by other subsystems. Variables are used for the data storage. Functional uniterms of the basic subsystem there are the following: extracting, substituting the xml code fragment, checking the separator kind in the uniterm, as well as the one checking if the uniterm is a number. Additionally there are included functional uniterms absorbing other uniterms, and the one informing on optimization process run. The basic and rendering data models are implemented in C# . In conclusions some advantages of both the algorithm formula optimization computer system and model decomposition into subsystem are given. Bibliography contains a set of intuitive and formal works describing the algorithms algebra.
EN
This paper aims to compare the performance of prediction for various linear models ARMA/ARIMA, under various forgetting factors, on the real-time series from the Internet traffic.
EN
This paper aims to compare the performance of prediction for various ARIMA models, under various sampling time and differencing operators.
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