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    Wyświetlanie 1-4 z 4
    Tytuł:
    Recurrent neural identification and control of a continuous bioprocess via first and second order learning
    Autorzy:
    Baruch, I.
    Mariaca-Gaspar, C. R.
    Powiązania:
    https://bibliotekanauki.pl/articles/385133.pdf
    Data publikacji:
    2010
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    backpropagation learning
    direct adaptive neural control
    indirect adaptive sliding mode control
    Kalman filter recurrent neural network identifier
    Levenberg-Marquardt learning
    Opis:
    This paper applies a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Mar quardt (L-M) learning algorithm capable to estimate para meters and states of highly nonlinear unknown plant in noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct and indirect adaptive neural con trol schemes. The proposed control schemes were applied for real-time recurrent neural identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 4; 37-52
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Remarks on some learning algorithms
    Uwagi na temat komputerowych algorytmów uczenia się
    Autorzy:
    Domański, Czesław
    Pekasiewicz, Dorota
    Powiązania:
    https://bibliotekanauki.pl/articles/904617.pdf
    Data publikacji:
    1997
    Wydawca:
    Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
    Tematy:
    algorithmic procedure
    heuristic procedure
    learning algorithms
    backpropagation algorithms
    Opis:
    In this paper we present some problems concerning artificial intelligence. In the first two points there are presented algorithmic and heuristic procedures, which are applied in solving problems and making optimal decision for the fixed states as follows: beginning state, set of all states of a problem, subset of final states. The fundamental part of the paper discusses three leaming-algorithms: ID3, AQ and backpropagation, owing to which computer basing on a given sample is to create a general formula or rule and to make a decision which is a solution of the problem. The last section of the paper includes a short description of the predictive accuracy of the algorithms.
    Źródło:
    Acta Universitatis Lodziensis. Folia Oeconomica; 1997, 141
    0208-6018
    2353-7663
    Pojawia się w:
    Acta Universitatis Lodziensis. Folia Oeconomica
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images
    Autorzy:
    Taha, Lamyaa Gamal El-deen
    Ibrahim, Rania Elsayed
    Powiązania:
    https://bibliotekanauki.pl/articles/1837996.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    ensemble classifiers
    machine learning
    random forest
    maximum likelihood
    support vector machines
    backpropagation
    image classification
    Opis:
    The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.
    Źródło:
    Geomatics and Environmental Engineering; 2021, 15, 4; 101-116
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    2D Cadastral Coordinate Transformation using extreme learning machine technique
    Autorzy:
    Ziggah, Y. Y.
    Issaka, Y.
    Laari, P. B.
    Hui, Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/145372.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    transformacja współrzędnych
    sieci neuronowe
    dane geodezyjne
    sieć radialna
    coordinate transformation
    extreme learning machine
    backpropagation neural network
    radial basis function neural network
    geodetic datum
    Opis:
    Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.
    Źródło:
    Geodesy and Cartography; 2018, 67, 2; 321-343
    2080-6736
    2300-2581
    Pojawia się w:
    Geodesy and Cartography
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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