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Wyszukujesz frazę "Rutkowska, D." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Perception-based reasoning: evaluation systems
Autorzy:
Rutkowska, D.
Powiązania:
https://bibliotekanauki.pl/articles/1931577.pdf
Data publikacji:
2003
Wydawca:
Politechnika Gdańska
Tematy:
fuzzy sets
perception-based systems
fuzzy neurons
neural networks
artificial intelligence
Opis:
A perception-based interpretation of evaluation systems is proposed in this paper. Thus, a perception-based approach to create intelligent systems is considered. The evaluation systems can be employed eg. in order to assess student exams, as well as to other applications. Evaluation marks used in these systems are given as perceptions expressed by words. The words play the role of labels of perceptions, and are represented by fuzzy sets. This means that the idea of perception-based systems, introduced by Zadeh, is applied. Various algorithms of overall assessment are suggested in this paper. Overall evaluation is produced as an aggregation of component evaluation marks. Systems of this kind can be obtained using fuzzy neurons, so fuzzy neural networks are also mentioned as a method of perception-based reasoning. The usefulness in artificial intelligence of both fuzzy sets and neural networks, and especially a combination of these, is shown.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2003, 7, 1; 131-145
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fuzzy inference neural networks with fuzzy parameters
Autorzy:
Rutkowska, D.
Hayashi, Y.
Powiązania:
https://bibliotekanauki.pl/articles/1931581.pdf
Data publikacji:
2003
Wydawca:
Politechnika Gdańska
Tematy:
neuro-fuzzy systems
fuzzy neural networks
fuzzy inference neural networks
fuzzy systems of type 2
fuzzy granulation
Opis:
This paper concerns fuzzy neural networks and fuzzy inference neural networks, which are two different approaches to neuro-fuzzy combinations. The former is a direct fuzzification of artificial neural networks by introducing fuzzy signals and fuzzy weights. The latter is a representation of fuzzy systems in the form of multi-layer connectionist networks, similar to neural networks. Parameters of membership functions (centers and widths) play the role of neural network weights. In this paper, fuzzy inference neural networks with fuzzy parameters are considered. Neuro-fuzzy systems of this kind utilize both approaches: fuzzy neural networks and fuzzy inference neural networks. They also pertain to fuzzy systems of type 2 since membership functions with fuzzy parameters characterize type 2 fuzzy sets. Various architectures of these networks have been obtained for fuzzy systems based on different fuzzy implications. By analogy with fuzzy inference neural networks with crisp parameters, methods of learning fuzzy parameters and rule generation can be derived for neuro-fuzzy systems with fuzzy parameters. Fuzzy inference neural networks are studied in the framework of fuzzy granulation. In particular, fuzzy clustering as fuzzy information granulation is proposed to be applied in order to generate fuzzy IF-THEN rules. Applications of fuzzy inference neural networks are also outlined.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2003, 7, 1; 7-22
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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