Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "pattern analysis" wg kryterium: Temat


Wyświetlanie 1-2 z 2
Tytuł:
Bayes classification of imprecise information of interval type
Autorzy:
Kulczycki, P.
Kowalski, P. A.
Powiązania:
https://bibliotekanauki.pl/articles/205655.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
data analysis
classification
imprecise information
interval type information
statistical kernel estimators
reduction in pattern size
classifier parameter correction
sensitivity method for artificial neural networks
Opis:
The subject of the investigation presented here is Bayes classification of imprecise multidimensional information of interval type by means of patterns defined through precise data, e.g. deterministic or sharp. For this purpose the statistical kernel estimators methodology was applied, which makes the resulting algorithm independent of the pattern shape. In addition, elements of pattern sets which have insignificant or negative influence on the correctness of classification are eliminated. The concept for realizing the procedure is based on the sensitivity method, used in the domain of artificial neural networks. As a result of this procedure the number of correct classifications and - above all - calculation speed increased significantly. A further growth in quality of classification was achieved with an algorithm for the correction of classifier parameter values. The results of numerical verification, carried out on pseudorandom and benchmark data, as well as a comparative analysis with other methods of similar conditioning, have validated the concept presented here and its positive features.
Źródło:
Control and Cybernetics; 2011, 40, 1; 101-123
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Temporal alarm pattern discovery in mobile telecommunication networks based on binary series analysis
Autorzy:
Mazdziarz, A.
Powiązania:
https://bibliotekanauki.pl/articles/205702.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
mobile telecommunication network
fault diagnosis
root cause analysis
Dice similarity coefficients
Hamming distance
temporal pattern mining
Opis:
Highly-advanced systems, such as mobile telecommunication networks, characterized by increased complexity, make maintenance routines difficult. Amount of data to be analyzed in a short time during fault diagnosis of the mobile telecommunication networks strongly justifies the need to automate alarm correlation and root cause analysis. A major challenge in the establishment of alarm correlation is to determine how to reflect the alarm flow inertia. Thus, adequate temporal alarm pattern discovery methods should be used in fault diagnosis for correlation-related purposes. Automatic temporal alarm pattern discovery allows fast generation of root cause analysis hypotheses and supports effective troubleshooting of network problems. The process for fault propagation throughout the network is manifested by the time lag between the root-cause alarm and potentially linked symptoms, as well as weakening correlation strength with time. The paper presents a novel method for alarm correlation analysis in mobile telecommunication networks, based on binary series analysis. The method allows for discovery of causal relationship between alarms with dynamic alarm correlation window size estimation.
Źródło:
Control and Cybernetics; 2018, 47, 2; 191-213
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies