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Wyszukujesz frazę "Tyburek, Krzysztof" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
An Expert System for Automatic Classification of Sound Signals
Autorzy:
Tyburek, Krzysztof
Kotlarz, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/307799.pdf
Data publikacji:
2020
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
audio descriptors
bird species
fuzzy classification of audio signals
MPEG-7
spectral features of sound
Opis:
In this paper, we present the results of research focusing on methods for recognition/classification of audio signals. We consider the results of the research project to serve as a basis for the main module of a hybrid expert system currently under development. In our earlier studies, we conducted research on the effectiveness of three classifiers: fuzzy classifier, neural classifier and WEKA system for reference data. In this project, a particular emphasis was placed on fine-tuning the fuzzy classifier model and on identifying neural classifier applications, taking into account new neural networks that we have not studied so far in connection with sounds classification methods.
Źródło:
Journal of Telecommunications and Information Technology; 2020, 2; 86-90
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fuzzy-based Description of Computational Complexity of Central Nervous Systems
Autorzy:
Prokopowicz, Piotr
Mikołajewski, Dariusz
Tyburek, Krzysztof
Kotlarz, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/309535.pdf
Data publikacji:
2020
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
cognitive deficit
cognitive functions
computational simulation
fuzzy descriptors
Opis:
Computational intelligence algorithms are currently capable of dealing with simple cognitive processes, but still remain inefficient compared with the human brain’s ability to learn from few exemplars or to analyze problems that have not been defined in an explicit manner. Generalization and decision-making processes typically require an uncertainty model that is applied to the decision options while relying on the probability approach. Thus, models of such cognitive functions usually interact with reinforcement-based learning to simplify complex problems. Decision-makers are needed to choose from the decision options that are available, in order to ensure that the decision-makers’ choices are rational. They maximize the subjective overall utility expected, given by the outcomes in different states and weighted with subjective beliefs about the occurrence of those states. Beliefs are captured by probabilities and new information is incorporated using the Bayes’ law. Fuzzy-based models described in this paper propose a different – they may serve as a point of departure for a family of novel methods enabling more effective and neurobiologically reliable brain simulation that is based on fuzzy logic techniques and that turns out to be useful in both basic and applied sciences. The approach presented provides a valuable insight into understanding the aforementioned processes, doing that in a descriptive, fuzzy-based manner, without presenting a complex analysis.
Źródło:
Journal of Telecommunications and Information Technology; 2020, 3; 57-66
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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