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Showing 161 - 180 results of 12,381 for search '(( algorithm python function ) OR ((( algorithm sphere function ) OR ( algorithm 1 function ))))', query time: 1.05s Refine Results
  1. 161
    MSEs for the adaptive filtering algorithms by using hyperbolic functions.

    MSEs for the adaptive filtering algorithms by using hyperbolic functions. by Sihai Guan (11547518)

    Published 2021
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  2. 162
    MSEs for the adaptive filtering algorithms by using hyperbolic functions.

    MSEs for the adaptive filtering algorithms by using hyperbolic functions. by Sihai Guan (11547518)

    Published 2021
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  3. 163
    MSEs for the adaptive filtering algorithms by using hyperbolic functions.

    MSEs for the adaptive filtering algorithms by using hyperbolic functions. by Sihai Guan (11547518)

    Published 2021
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  4. 164
    Algorithm results based on FE simulated likelihood functions.

    Algorithm results based on FE simulated likelihood functions. by Nicolas Herzig (9230425)

    Published 2020
    “…<p>(A) Nodule depth estimation by the algorithm with the likelihood functions obtained by FEM simulation. …”
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  5. 165
    An example of our color correction algorithm (with primary surface function <i>f</i><sub>1</sub>).

    An example of our color correction algorithm (with primary surface function <i>f</i><sub>1</sub>). by Guixiang Zhang (383795)

    Published 2023
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  6. 166
    WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).

    WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05). by Ruyi Dong (9038174)

    Published 2025
    “…<p>WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).…”
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  7. 167
    WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).

    WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05). by Ruyi Dong (9038174)

    Published 2025
    “…<p>WST results for LLSKSO and other algorithms on benchmark test functions. (<i>α</i> = 0.05).…”
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  8. 168
    Loss function variation curve.

    Loss function variation curve. by Chunhua Yang (346871)

    Published 2025
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  9. 169
    Difference between the mean of the distances inside the first peak and the minimum distance.

    Difference between the mean of the distances inside the first peak and the minimum distance. by Luca Maria Del Bono (20479625)

    Published 2024
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  10. 170
    Standard deviation of distances inside the first peak.

    Standard deviation of distances inside the first peak. by Luca Maria Del Bono (20479625)

    Published 2024
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  11. 171
    Standard deviation over mean of the distances ratio.

    Standard deviation over mean of the distances ratio. by Luca Maria Del Bono (20479625)

    Published 2024
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  12. 172
    Structure factor for LP, PCS and MSP.

    Structure factor for LP, PCS and MSP. by Luca Maria Del Bono (20479625)

    Published 2024
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  13. 173
    Mean distance inside the first peak.

    Mean distance inside the first peak. by Luca Maria Del Bono (20479625)

    Published 2024
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  14. 174
    Simulations details for the Rosenbrock function.

    Simulations details for the Rosenbrock function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  15. 175
    Simulations details for the Rastrigin function.

    Simulations details for the Rastrigin function. by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  16. 176
    Non-converging numerical solution of the non-linear equation system through the iterations <i>p</i> using the Newton-Raphson algorithm and the Levenberg–Marquardt algorithm.

    Non-converging numerical solution of the non-linear equation system through the iterations <i>p</i> using the Newton-Raphson algorithm and the Levenberg–Marquardt algorithm. by Martin Dodek (20623832)

    Published 2025
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  17. 177
    F1-scores of anomaly detection algorithms.

    F1-scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

    Published 2025
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  18. 178
    Global optima for objective functions <i>f</i><sub>1</sub> –<i>f</i><sub>14</sub>.

    Global optima for objective functions <i>f</i><sub>1</sub> –<i>f</i><sub>14</sub>. by Panagiotis Aivaliotis-Apostolopoulos (13851171)

    Published 2022
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  19. 179
    Tunable parameters in stimuli generation module. <sup><i>a</i></sup>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></sup>Different stimuli are displayed equally. If desired by the user, some stimuli can be displayed more/less frequently by assigning them a higher/lower weight.

    Tunable parameters in stimuli generation module. <sup><i>a</i></sup>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

    Published 2025
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  20. 180
    Benchmark functions.

    Benchmark functions. by Xiaowen Zhang (83669)

    Published 2023
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