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يعرض 1 - 18 نتائج من 18 نتيجة بحث عن '(((( algorithm basis function ) OR ( algorithm gpcr function ))) OR ( algorithm python function ))', وقت الاستعلام: 0.10s تنقيح النتائج
  1. 1

    Radial basis function networks for contingency analysis of bulkpower systems حسب Refaee, J.A.

    منشور في 1999
    "…Radial basis function networks (RBFNs) are used for contingency evaluation of bulk power system. …"
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    article
  2. 2

    A fuzzy basis function network for generator excitation control حسب Abido, M.A.

    منشور في 1997
    "…A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper. …"
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    article
  3. 3

    A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models حسب Osama Bassam J. Rabie (21323741)

    منشور في 2024
    "…For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. …"
  4. 4

    BUC algorithm for iceberg cubes حسب Nasr, George E.

    منشور في 2003
    "…In this paper, we implement the Bottom-Up Computation (BUC) algorithm for computing Iceberg cubes and conduct a sensitivity analysis of BUC with respect to the probability density function of the data. …"
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    conferenceObject
  5. 5

    Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm حسب Youssef, H.

    منشور في 2020
    "…The overall cost function has been developed using fuzzy logic. Several variants of the algorithm are proposed and compared together via simulation and experimental results are provided. r 2002 Elsevier Science Ltd. …"
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    article
  6. 6

    Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm حسب Youssef, H.

    منشور في 2020
    "…The overall cost function has been developed using fuzzy logic. Several variants of the algorithm are proposed and compared together via simulation and experimental results are provided. r 2002 Elsevier Science Ltd. …"
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    article
  7. 7

    Rigorous Phase Equilibrium Calculation Methods for Strong Electrolyte Solutions: The Isothermal Flash حسب Ilias K. Nikolaidis (9217172)

    منشور في 2022
    "…In this way, an augmented function (Lagrange function) is formulated which serves as the basis for the equations that govern phase equilibrium. …"
  8. 8

    An Artificial Neural Network for Online Tuning of Genetic Algorithm Based PI Controller for Interior Permanent Magnet Synchronous Motor–Drive حسب Rahman, M. A.

    منشور في 2006
    "…In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A radial basis function network (RBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. …"
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    article
  9. 9

    Android Malware Detection Using Machine Learning حسب Al Ali, Shaikha

    منشور في 2024
    "…It was found that SVM using radial basis function (RBF) kernel achieved the highest performance with an accuracy of 99.5%. …"
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    article
  10. 10

    Contingency analysis of bulk power system using neural networks حسب Maghrabi, H.

    منشور في 1998
    "…Radial basis function networks (RBFNs) are used for the contingency evaluation of bulk power systems. …"
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    article
  11. 11

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

    منشور في 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. …"
  12. 12

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

    منشور في 1998
    "…A fuzzy basis function network (FBFN) based power system stabilizer (PSS) is presented in this paper to improve power system dynamic stability. …"
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    article
  13. 13

    Development and Implementation of a Hybrid Intelligent Controller for Interior Permanent Magnet Synchronous Motor Drives حسب Nasir Uddin, M.

    منشور في 2004
    "…In the optimization procedure a performance index is developed to reflect the minimum speed deviation, minimum settling time and zero steady-state error. A fuzzy basis function network (FBFN) is utilized for online tuning of the PI controller parameters to ensure optimum drive performance under different disturbances. …"
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    article
  14. 14

    Design and performance analysis of hybrid MPPT controllers for fuel cell fed DC-DC converter systems حسب Shaik Rafikiran (15838929)

    منشور في 2023
    "…The studied MPPT controllers are Adaptive Adjustable Step-based Perturb and Observe (AASP&O) controllers, Variable Step Value-Radial Basis Function Controller (VSV-RBFC), Adaptive Step Hill Climb (ASHC) based fuzzy technique, Variable P&O with Particle Swarm Optimization (VP&O-PSO), and Variable Step Grey Wolf Algorithm (VSGWA) based fuzzy logic controller. …"
  15. 15

    Design and performance analysis of hybrid MPPT controllers for fuel cell fed DC-DC converter systems حسب Shaik, Rafikiran

    منشور في 2023
    "…The studied MPPT controllers are Adaptive Adjustable Step-based Perturb and Observe (AAS-P&O) controllers, Variable Step Value-Radial Basis Function Controller (VSV-RBFC), Adaptive Step Hill Climb (ASHC) based fuzzy technique, Variable P&O with Particle Swarm Optimization (VP&O-PSO), and Variable Step Grey Wolf Algorithm (VSGWA) based fuzzy logic controller. …"
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    article
  16. 16
  17. 17

    Wiener-Hammerstein Model Identification-Recursive lgorithms حسب Emara-Shabaik, Husam

    منشور في 2020
    "…These algorithms are derived on the basis of minimizing cost functions of the output errors, the equation errors, and the prediction errors. …"
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    article
  18. 18

    FoGMatch حسب Arisdakessian, Sarhad

    منشور في 2019
    "…Our solution consists of (1) two optimization problems, one for the IoT devices and one for the fog nodes, (2) preference functions for both the IoT and fog layers to help them rank each other on the basis of several criteria such latency and resource utilization, and (3) centralized and distributed intelligent scheduling algorithms that consider the preferences of both the fog and IoT layers to improve the performance of the overall IoT ecosystem. …"
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    masterThesis