Search Results - (( motion optimization method algorithm ) OR ( parallel optimization learning algorithm ))

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  1. 1

    Design and implemtation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayyawi by Jabbar Hayyawi, Mustafa

    Published 2016
    “…Performance indices such as workspace, dexterity and stiffness, of the parallel manipulator are studied. The parallel manipulator is optimized based on the performance indices to obtain on the optimal design parameters for achieved maximum performance of the parallel manipulator. …”
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    Student Project
  2. 2

    Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms by Loo, C.K., Liew, W.S., Seera, M., Lim, Einly

    Published 2015
    “…A genetic algorithm search heuristic was chosen to solve this multi-objective optimization problem. …”
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    Article
  3. 3

    Design and implementation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayawi by Hayawi, Mustafa Jabbar

    Published 2015
    “…Performance indices such as workspace, dexterity and stiffness, of the parallel manipulator are studied. The parallel manipulator is optimized based on the performance indices to obtain on the optimal design parameters for achieved maximum performance of the parallel manipulator. …”
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    Thesis
  4. 4

    PMT : opposition based learning technique for enhancing metaheuristic algorithms performance by Hammoudeh, S. Alamri

    Published 2020
    “…Metaheuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. …”
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    Thesis
  5. 5

    Hexagon pattern particle swarm optimization based block matching algorithm for motion estimation / Siti Eshah Che Osman by Che Osman, Siti Eshah

    Published 2019
    “…Recently, intelligent searching methods were proposed to enhance the computational optimization issues in motion estimation but still lack in obtaining the best solution of block matching. …”
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    Thesis
  6. 6

    Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks by Shing, Chiang Ta, Mohammed Al-Andoli, Mohammed Nasser, Wooi, Ping Cheah

    Published 2022
    “…Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. …”
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    Article
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    A parallel ensemble learning model for fault detection and diagnosis of industrial machinery by Shing, Chiang Ta, Mohammed Al-Andoli, Mohammed Nasser, Kok, Swee Sim, Seera, Manjeevan, Chee, Peng Lim

    Published 2023
    “…However, the gradient descent optimization method that is commonly used in deep learning suffers from several limitations, such as high computational cost and local sub-optimal solutions. …”
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    Article
  10. 10

    PMT: opposition-based learning technique for enhancing meta-heuristic performance by Alamri, Hammoudeh S., Kamal Z., Zamli

    Published 2019
    “…Meta-heuristic algorithms have shown promising performance in solving sophisticated real-world optimization problems. …”
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    Article
  11. 11

    A Metaheuristic Optimization Using Explosion Method On A Hybrid Pd2-Lqr Quadcopter Controller by Raihan, Mohamad Norherman Shauqie Mohamed

    Published 2021
    “…Therefore, an optimization algorithm based on the explosion method called REA was proposed and implemented on the proposed Hybrid PD2-LQR control structure. …”
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    Thesis
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    Multilevel optimization for dense motion estimation by Saaban, Azizan, Kalmoun, El Mostafa, Ibrahim, Haslinda, Ramli, Razamin, Omar, Zurni

    Published 2011
    “…We evaluated the performance of different optimization techniques developed in the context of optical flow computation with different variational models.In particular, based on truncated Newton methods (TN) that have been an effective approach for large-scale unconstrained optimization, we developed the use of efficient multilevel schemes for computing the optical flow.More precisely, we evaluated the performance of a standard unidirectional multilevel algorithm - called multiresolution optimization (MR/Opt), to a bidrectional multilevel algorithm - called full multigrid optimization (FMG/Opt).The FMG/Opt algorithm treats the coarse grid correction as an optimization search direction and eventually scales it using a line search. …”
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    Monograph