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    Wyświetlanie 1-8 z 8
    Tytuł:
    Signature recognition with a hybrid approach combining modular neural networks and fuzzy logic for response integration
    Autorzy:
    Beltrán, M.
    Melin, P.
    Trujillo, L.
    Lopez, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/384541.pdf
    Data publikacji:
    2010
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    pattern recognition
    neural networks
    fuzzy logic
    Opis:
    This paper describes a modular neural network (MNN) with fuzzy integration for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community. For instance, many attempts have been made in order to automate the process of identifying a person’s handwritten signature; however this problem has proven to be a very difficult task. In this work, we propose a MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined using a Sugeno fuzzy integral and a fuzzy inference system. The experimental results obtained using a database of 30 individual’s shows that the modular architecture can achieve a very high 99.33% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system. Furthermore we consider the verification of signatures as false acceptance, false rejection and error recognition of the MNN.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 1; 20-27
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Classification, Association and Pattern Completion Using Neural Similarity Based Methods
    Autorzy:
    Duch, W.
    Adamczak, R.
    Diercksen, G. H. F.
    Powiązania:
    https://bibliotekanauki.pl/articles/911147.pdf
    Data publikacji:
    2000
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć neuronowa
    klasyfikacja
    rozpoznawanie obrazów
    neural networks
    classification
    association
    pattern recognition
    Opis:
    A framework for Similarity-Based Methods (SBMs) includes many classification models as special cases: neural networks of the Radial Basis Function type, Feature Space Mapping neurofuzzy networks based on separable transfer functions, Learning Vector Quantization, variants of the k nearest neighbor methods and several new models that may be presented in a network form. Multilayer Perceptrons (MLPs) use scalar products to compute a weighted activation of neurons, combining soft hyperplanes to provide decision borders. Distance-based multilayer perceptrons (D-MLPs) evaluate the similarity of inputs to weights offering a natural generalization of standard MLPs. A cluster- based initialization procedure determining the architecture and values of all adaptive parameters is described. Networks implementing SBM methods are useful not only for classification and approximation, but also as associative memories, in problems requiring pattern completion, offering an efficient way to deal with missing values. Non-Euclidean distance functions may also be introduced by normalization of the input vectors in an extended feature space. Both the approaches dramatically influence the shapes of decision borders. An illustrative example showing these changes is provided.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2000, 10, 4; 747-766
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    HBT analysis in ALICE with ITS stand-alone and combined neural tracking (preliminary results)
    Autorzy:
    Badala, A.
    Barbera, R.
    Lo Re, G.
    Palmeri, A.
    Pappalardo, G.
    Pulvirenti, A.
    Riggi, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/147436.pdf
    Data publikacji:
    2004
    Wydawca:
    Instytut Chemii i Techniki Jądrowej
    Tematy:
    intensity interferometry (HBT)
    neural networks
    pattern recognition
    track reconstruction
    ALICE experiment
    Opis:
    A neural network based algorithm to perform track recognition in the ALICE Inner Tracking System (ITS) for high transverse momentum particles (pt > 1 GeV/c) is presented. The model is based on the Denby-Peterson scheme, with some original improvements which are necessary to cope with the large track density expected at ALICE. Results are shown for central Pb-Pb events at 5.5 ATeV in the center of mass system and the comparison with the Kalman filter results is included. Data coming from this tracking procedure are used for 1-dimensional HBT correlations and results are presented.
    Źródło:
    Nukleonika; 2004, 49,suppl.2; 95-98
    0029-5922
    1508-5791
    Pojawia się w:
    Nukleonika
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Combining Rough Sets and Neural Network Approaches in Pattern Recognition
    Autorzy:
    Cyran, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/92799.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
    Tematy:
    pattern recognition
    neural networks
    rough sets
    hybrid methods
    evolutionary optimization
    holographic ring-wedge detector
    Opis:
    The paper focuses on problems which arise when two different types of AI methods are combined in one design. The first type is rule based, rough set methodology operating is highly discretized attribute space. The discretization is a consequence of the granular nature of knowledge representation in the theory of rough sets. The second type is neural network working in continuous space. Problems of combining these different types of knowledge processing are illustrated in a system used for recognition of diffraction patterns. The feature extraction is performed with the use of holographic ring wedge detector, generating the continuous feature space. No doubt, this is a feature space natural for application of the neural network. However, the criterion of optimization of the feature extractor uses rough set based knowledge representation. This latter, requires the discretization of conditional attributes generating the feature space. The novel enhanced method of optimization of holographic ring wedge detector is proposed, as a result of modification of indiscernibility relation in the theory of rough sets.
    Źródło:
    Studia Informatica : systems and information technology; 2005, 2(6); 7-20
    1731-2264
    Pojawia się w:
    Studia Informatica : systems and information technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Wykorzystanie sztucznych sieci neuronowych do wykrywania i rozpoznawania tablic rejestracyjnych na zdjęciach pojazdów
    Detection and recognition of registration plates on pictures of vehicles using artificial neural network
    Autorzy:
    Huzarek, M.
    Rutkowski, T. A.
    Powiązania:
    https://bibliotekanauki.pl/articles/267795.pdf
    Data publikacji:
    2015
    Wydawca:
    Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
    Tematy:
    przetwarzanie obrazu
    lokalizacja obiektów
    rozpoznawanie wzorców
    sieci neuronowe
    image processing
    object localization
    pattern recognition
    neural networks
    Opis:
    W artykule przedstawiono koncepcję algorytmu wykrywania i rozpoznawania tablic rejestracyjnych (AWiRTR) na obrazach cyfrowych pojazdów. Detekcja i lokalizacja tablic rejestracyjnych oraz wyodrębnienie z obrazu tablicy rejestracyjnej poszczególnych znaków odbywa się z wykorzystaniem podstawowych technik przetwarzania obrazu (przekształcenia morfologiczne, wykrywanie krawędzi) jak i podstawowych danych statystycznych obiektów wykrytych w obrazie (np. stosunek szerokość do wysokość obiektu). Natomiast za rozpoznawanie poszczególnych znaków odpowiedzialna jest wielowarstwowa, jednokierunkowa sztuczna sieć neuronowa. Przedstawiony algorytm został zaimplementowany i zweryfikowany w środowisku Matlab/Simulink. Pomimo wykorzystania w algorytmie AWiRTR dobrze znanych z literatury metod lokalizacji, segmentacji i rozpoznawania wzorców, otrzymane w trakcie weryfikacji algorytmu wyniki wskazują jego efektywność na poziomie 96,26%. Jest ona porównywalna do efektywności innych algorytmów AWiRTR opisywanych w literaturze.
    A license plate detection and recognition system has basically three modules for: localization of the plate region using the digital image of the car, extraction of the characters from digital image of the license plate, and recognition of the characters using a suitable identification method. In this paper, an algorithm is designed that can localize of the plate and extract of the characters from digital image of the license plate with the basics image processing techniques (morphological transformations, edge detection) and with the statistical data (e.g. width height ratio) of the objects identified in the analyzed digital image. It is done at the second and third stage of the presented algorithm, respectively. Finally, at the fourth stage of the presented algorithm, the character recognition is done by multilayer, one directional artificial neural network. Algorithm was implemented and verified in the Matlab/Simulink environment. Experimental results demonstrate promising efficiency of the proposed algorithm: 98% in the task of license plate localization, 95,69% in the task of characters extraction, and 95,11% in the task of characters recognition.
    Źródło:
    Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2015, 47; 67-70
    1425-5766
    2353-1290
    Pojawia się w:
    Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of multi-parameter data visualization by means of autoassociative neural networks to evaluate classification possibilities of various coal types
    Autorzy:
    Jamroz, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/109902.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    autoassociative neural networks
    coal types
    multidimensional visualization
    multi-parameter
    identification of data
    pattern recognition
    neural networks
    Opis:
    The significance of data visualization in modern research is growing steadily. In mineral processing scientists have to face many problems with understanding data and finding essential variables from a large amount of data registered for material or process. Hence it is necessary to apply visualization of such data, especially when a set of data is multi-parameter and very complex. This paper puts forward a proposal to introduce the autoassociative neural networks for visualization of data concerning three various types of hard coal. Apart from theoretical discussion of the method, the empirical applications of the method are presented. The results revealed that it is a useful tool for a researcher facing a complicated set of data which allows for its proper classification. The optimal neural network parameters to successfully separate the analyzed three types of coal were found out for the analyzed example.
    Źródło:
    Physicochemical Problems of Mineral Processing; 2014, 50, 2; 719-734
    1643-1049
    2084-4735
    Pojawia się w:
    Physicochemical Problems of Mineral Processing
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligence in manufacturing systems: the pattern recognition perspective
    Autorzy:
    Zaremba, M. B.
    Powiązania:
    https://bibliotekanauki.pl/articles/971032.pdf
    Data publikacji:
    2010
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    Intelligent Manufacturing Systems
    pattern recognition
    computational intelligence
    neural networks
    distributed systems
    spatial filtering
    feature selection
    dimensionality reduction
    Opis:
    The field of Intelligent Manufacturing Systems (IMS) has been generally equated with the use of Artificial Intelligence and Computational Intelligence methods and techniques in the design and operation of manufacturing systems. Those methods and techniques are now applied in many different technological domains to deal with such pervasive problems as data imprecision and nonlinear system behavior. The focus in IMS is now shifting to a broader understanding of the intelligent behavior of manufacturing systems. The questions debated by researchers today relate more to what kind and what level of adaptability to instill in the structure and operation of a manufacturing system, with the discussions increasingly gravitating to the issue of system self-organization. This paper explores the changing face of IMS from the perspective of the pattern recognition domain. It presents design criteria for techniques that will allow us to implement manufacturing systems exhibiting adaptive and intelligent behaviour. Examples are given to show how incorporating pattern recognition capabilities can help us build more intelligence and self-organization into the manufacturing systems of the future.
    Źródło:
    Control and Cybernetics; 2010, 39, 1; 233-258
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    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
      Wyświetlanie 1-8 z 8

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