Showing 81 - 100 results of 116 for search '(( algorithm fa function ) OR ( ((algorithm density) OR (algorithm design)) function ))', query time: 0.15s Refine Results
  1. 81

    Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation by Hariprasath Manoharan (14157966)

    Published 2023
    “…Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. …”
  2. 82

    IMPROVING BER PERFORMANCE OF LDPC CODES BASED ON INTERMEDIATE DECODING RESULTS by Alghonaim, Esa

    Published 2007
    “…The paper presents a novel approach to reduce the bit error rate (BER) in iterative belief propagation (BP) decoding of low density parity check (LDPC) codes. The behavior of the BP algorithm is first investigated as a function of number of decoder iterations, and it is shown that typical uncorrected error patterns can be classified into 3 categories: oscillating, nearly-constant, or random-like, with a predominance of oscillating patterns at high Signal-to-Noise (SNR) values. …”
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  3. 83
  4. 84

    Timing influenced general-cell genetic floorplanner by Sait, Sadiq M.

    Published 2020
    “…In this paper we present a timing-influenced floorplanner for general cell IC design. The floorplanner works in two phases. In the first phase we use the genetic algorithm and restrict the modules to be rigid and the floorplan to have slicing structure. …”
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    article
  5. 85

    Fast force-directed/simulated evolution hybrid for multiobjective VLSI cell placement by Sait, Sadiq M.

    Published 2004
    “…In this work, a fast hybrid algorithm is designed to address this problem. The algorithm employs simulated evolution (SE), an iterative search heuristic that comprises three steps: evaluation, selection and allocation. …”
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    article
  6. 86

    Adaptive cooperative control of nonlinear multi-agent systems with uncertain time-varying control directions and dead-zone nonlinearity by Maryam Shahriari-kahkeshi (17269051)

    Published 2021
    “…Then, by using the dynamic surface control approach and radial-basis function neural network, an adaptive distributed controller is designed for each follower agent. …”
  7. 87

    The Use of Enumerative Techniques in Topological Optimization of Computer Networks Subject to Fault Tolerance and Reliability by Abd-El-barr, Mostafa

    Published 2003
    “…Fault tolerance is an important network design aspect. A fault tolerant network is able to function even in the presence of some faults in the network. …”
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    article
  8. 88

    Analysis of power system stability enhancement via excitation and FACTS-based stabilizers by Abido, M. A.

    Published 2004
    “…Then, a real-coded genetic algorithm is employed to search for optimal controller parameters. …”
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  9. 89
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  11. 91

    Online Control and Optimization of Directional Drilling by unknown

    Published 2020
    “…The objective function considered is to minimize the tracking error and drilling efforts. …”
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    masterThesis
  12. 92

    A simplified sliding‐mode control method for multi‐level transformerless DVR by Hasan Komurcugil (16388513)

    Published 2022
    “…Second, an effective method based on charging/discharging conditions of DC capacitors is proposed for balancing capacitor voltages using relevant switching state rather than combining DC voltage error with the inductor current error through a suitable weighting factor in forming the cost function. Therefore, the weighting factor necessity in the control algorithm is eliminated. …”
  13. 93

    Enhanced Microgrid Reliability Through Optimal Battery Energy Storage System Type and Sizing by Mohammadreza Gholami (17032317)

    Published 2023
    “…<p dir="ltr">Reliability plays a crucial role in the design and implementation of microgrids (MGs). The integration of battery energy storage systems (BESSs) with renewable energies has been proposed as a solution to enhance reliability. …”
  14. 94

    Small-Signal Stability Analysis and Parameters Optimization of Virtual Synchronous Generator for Low-Inertia Power System by Alaa Altawallbeh (22565837)

    Published 2025
    “…We further propose a hybrid Particle Swarm Optimization (PSO) algorithm with a multi-objective cost function to optimize VSG controller gains. …”
  15. 95

    A hybrid neuro-fuzzy power system stabilizer for multimachine powersystems by Abido, M.A.

    Published 1998
    “…The orthogonal least squares (OLS) learning algorithm is developed for designing an adequate and parsimonious FBFN model. …”
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  16. 96

    Traffic Offloading with Channel Allocation in Cache-Enabled Ultra-Dense Wireless Networks by Abbas, Nadine

    Published 2018
    “…We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity.…”
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  17. 97

    Iterative heuristics for multiobjective VLSI standard cellplacement by Sait, Sadiq M.

    Published 2001
    “…Fuzzy rules are incorporated in order to design a multiobjective cost function that integrates the costs of three objectives in a single overall cost value. …”
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  18. 98

    EVOLUTIONARY HEURISTICS FOR MULTIOBJECTIVE VLSI NETLIST BI-PARTITIONING by Sait, Sadiq M.

    Published 2020
    “…Fuzzy rules are incorporated in order to design a multiobjective cost function. Both the techniques are applied to ISCAS-85/89 benchmark circuits and experimental results are reported and compared.…”
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    article
  19. 99

    Software defect prediction. (c2019) by Moussa, Rebecca

    Published 2019
    “…A module is a class in the object-oriented design or a function in the procedural design. The fault-proneness of a module is de ned as the probability of it containing defect and/or resulting in faults. …”
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    masterThesis
  20. 100

    On-demand deployment of multiple aerial base stations for traffic offloading and network recovery by Sharafeddine, Sanaa

    Published 2019
    “…We present performance results for the proposed algorithm as a function of various system parameters and demonstrate its effectiveness compared to the close-to-optimal greedy approach and its superiority compared to recent related work from the literature.…”
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