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    Wyświetlanie 1-3 z 3
    Tytuł:
    Robust H∞ control for a class of uncertain neutral systems with both state and control input time-varying delays via a unified LMI optimization approach
    Autorzy:
    Chen, J. D.
    Yang, C. D.
    Lin, K. J.
    Lien, C. H.
    Powiązania:
    https://bibliotekanauki.pl/articles/970609.pdf
    Data publikacji:
    2008
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    robust H[infinity] control
    neutral systems
    unified LMI approach
    convex optimization approach
    delay-dependent criterion
    Opis:
    The robust H∞ control problem is considered for a class of uncertain neutral system involving both state and control input time-varying delays. The uncertainties under consideration are nonlinear time-varying parameter perturbations. The methodology is based on the Lyapunov functional combined with a unified LMI approach, and a new delay-dependent criterion is proposed to guarantee the stabilization and disturbance attenuation of systems. Moreover, a convex optimization approach is used to solve the robust H∞ control disturbance attenuation problem. Finally, a numerical example is illustrated to show the validity of this paper. The simulation results reveal significant improvement over the recent results.
    Źródło:
    Control and Cybernetics; 2008, 37, 3; 517-530
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A hybrid PSO approach for solving non-convex optimization problems
    Autorzy:
    Ganesan, T.
    Vasant, P.
    Elamvazuthy, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/229756.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Kuhn-Tucker conditions (KT)
    non-convex optimization
    particle swarm optimization (PSO)
    semi-classical particle swarm optimization (SPSO)
    Opis:
    The aim of this paper is to propose an improved particle swarm optimization (PSO) procedure for non-convex optimization problems. This approach embeds classical methods which are the Kuhn-Tucker (KT) conditions and the Hessian matrix into the fitness function. This generates a semi-classical PSO algorithm (SPSO). The classical component improves the PSO method in terms of its capacity to search for optimal solutions in non-convex scenarios. In this work, the development and the testing of the refined the SPSO algorithm was carried out. The SPSO algorithm was tested against two engineering design problems which were; ‘optimization of the design of a pressure vessel’ (P1) and the ‘optimization of the design of a tension/compression spring’ (P2). The computational performance of the SPSO algorithm was then compared against the modified particle swarm optimization (PSO) algorithm of previous work on the same engineering problems. Comparative studies and analysis were then carried out based on the optimized results. It was observed that the SPSO provides a better minimum with a higher quality constraint satisfaction as compared to the PSO approach in the previous work.
    Źródło:
    Archives of Control Sciences; 2012, 22, 1; 87-105
    1230-2384
    Pojawia się w:
    Archives of Control Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fast edge detection approach based on global optimization convex model and Split Bregman algorithm
    Autorzy:
    Jing, Y.
    Liu, J.
    Liu, Z.
    Cao, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/329158.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
    Tematy:
    edge detection
    active contour
    global optimization
    numerical minimization
    split Bregman algorithm
    detekcja krawędzi
    kontur aktywny
    optymalizacja globalna
    algorytm Bregmana
    Opis:
    Active contour model is a typical and effective closed edge detection algorithm, which has been widely applied in remote sensing image processing. Since the variety of the image data source, the complexity of the application background and the limitations of edge detection, the robustness and universality of active contour model are greatly reduced in the practical application of edge extraction. This study presented a fast edge detection approach based on global optimization convex model and Split Bregman algorithm. Firstly, the proposed approach defined a generalized convex function variational model which incorporated the RSF model’s principle and Chan’s global optimization idea and could get the global optimal solution. Secondly, a fast numerical minimization scheme based on split Bregman iterative algorithm is employed for overcoming drawbacks of noise and others. Finally, the curve evolves to the target boundaries quickly and accurately. The approach was applied in real special sea ice SAR images and synthetic images with noise, fuzzy boundaries and intensity inhomogeneity, and the experiment results showed that the proposed approach had a better performance than the edge detection methods based on the GMAC model and RSF model. The validity and robustness of the proposed approach were also verified.
    Źródło:
    Diagnostyka; 2018, 19, 2; 23-29
    1641-6414
    2449-5220
    Pojawia się w:
    Diagnostyka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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