Informacja

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

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "fuzzy-neural networks" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-4 z 4
    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
    Tytuł:
    Membership function - ARTMAP neural networks
    Autorzy:
    Sinčák, P.
    Hric, M.
    Vaščák, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/1931570.pdf
    Data publikacji:
    2003
    Wydawca:
    Politechnika Gdańska
    Tematy:
    pattern recognition principles
    classifier design
    classification accuracy assessment
    contingency tables
    backpropagation neural networks
    fuzzy BP neural networks
    ART and ARTMAP neural networks
    modular neural networks
    neural networks
    Opis:
    The project deals with the application of computational intelligence (CI) tools for multispectral image classification. Pattern Recognition scheme is a global approach where the classification part is playing an important role to achieve the highest classification accuracy. Multispectral images are data mainly used in remote sensing and this kind of classification is very difficult to assess the accuracy of classification results. There is a feedback problem in adjusting the parts of pattern recognition scheme. Precise classification accuracy assessment is almost impossible to obtain, being an extremely laborious procedure. The paper presents simple neural networks for multispectral image classification, ARTMAP-like neural networks as more sophisticated tools for classification, and a modular approach to achieve the highest classification accuracy of multispectral images. There is a strong link to advances in computer technology, which gives much better conditions for modelling more sophisticated classifiers for multispectral images.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2003, 7, 1; 43-52
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    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:
    Beta neuro-fuzzy systems
    Autorzy:
    Alimi, A. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/1931568.pdf
    Data publikacji:
    2003
    Wydawca:
    Politechnika Gdańska
    Tematy:
    beta function
    kernel based neural networks
    Sugeno fuzzy model
    neuro-fuzzy systems
    universal approximation property
    learning algorithms
    incremental learning
    Opis:
    In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central-limit theorem, is also given. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzzy basis functions for the design of fuzzy logic systems. The functional equivalence between Beta-based function neural networks and Beta fuzzy logic systems is then shown with the introduction of Beta neuro-fuzzy systems. By using the SW theorem and expanding the output of the Beta neuro-fuzzy system into a series of Beta fuzzy-based functions, we prove that one can uniformly approximate any real continuous function on a compact set to any arbitrary accuracy. Finally, a learning algorithm of the Beta neuro-fuzzy system is described and illustrated with numerical examples.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2003, 7, 1; 23-41
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

      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