Showing 1 - 18 results of 18 for search '(( binary based whole optimization algorithm ) OR ( single layer wolf optimization algorithm ))', query time: 0.45s Refine Results
  1. 1

    Comparison of optimization algorithms. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  2. 2

    Algorithm comparison. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  3. 3

    Process of GWO optimization. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
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  5. 5

    <i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

    Published 2023
    “…The sequences can include from a single SNP-allele pair up to a maximum number of pairs defined by the user (<i>l</i><sub>max</sub>). <b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …”
  6. 6

    . Fitness curve. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  7. 7

    Partial faults features. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  8. 8

    Diagram of faults identification. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  9. 9

    Confusion matrix. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  10. 10

    Sample group. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  11. 11

    Data in the experiment. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  12. 12

    Diagram of attention mechanism. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  13. 13

    Accuracy curve. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  14. 14

    Structure of MLP. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  15. 15

    Fault recording signal. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  16. 16

    Ablation study. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  17. 17

    Dual-channel MLP-Attention model. by Ning Ji (325849)

    Published 2024
    “…<div><p>In order to improve the accuracy of the transformer fault identification using nature-inspired algorithms, an identification method based on the GWO (Grey Wolf Optimizer)-optimized Dual-channel MLP (Multilayer Perceptron)-Attention is proposed. …”
  18. 18

    Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

    Published 2022
    “…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”