بدائل البحث:
based optimization » whale optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
binary gene » primary gene (توسيع البحث)
layer wolf » layer self (توسيع البحث), layer mols (توسيع البحث)
based optimization » whale optimization (توسيع البحث)
wolf optimization » whale optimization (توسيع البحث), swarm optimization (توسيع البحث), _ optimization (توسيع البحث)
binary gene » primary gene (توسيع البحث)
layer wolf » layer self (توسيع البحث), layer mols (توسيع البحث)
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1
Comparison of optimization algorithms.
منشور في 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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2
Algorithm comparison.
منشور في 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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3
Process of GWO optimization.
منشور في 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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4
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5
. Fitness curve.
منشور في 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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6
Partial faults features.
منشور في 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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7
Diagram of faults identification.
منشور في 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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8
Confusion matrix.
منشور في 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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9
Sample group.
منشور في 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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10
Data in the experiment.
منشور في 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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11
Diagram of attention mechanism.
منشور في 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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12
Accuracy curve.
منشور في 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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13
Structure of MLP.
منشور في 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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14
Fault recording signal.
منشور في 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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15
Ablation study.
منشور في 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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16
Dual-channel MLP-Attention model.
منشور في 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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17
GSE96058 information.
منشور في 2024"…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …"
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18
The performance of classifiers.
منشور في 2024"…Subsequently, feature selection was conducted using ANOVA and binary Particle Swarm Optimization (PSO). During the analysis phase, the discriminative power of the selected features was evaluated using machine learning classification algorithms. …"
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19
Table_1_Computational prediction of promotors in Agrobacterium tumefaciens strain C58 by using the machine learning technique.DOCX
منشور في 2023"…In the model, promotor sequences were encoded by three different kinds of feature descriptors, namely, accumulated nucleotide frequency, k-mer nucleotide composition, and binary encodings. The obtained features were optimized by using correlation and the mRMR-based algorithm. …"
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20
Models and Dataset
منشور في 2025"…</p><p dir="ltr"><br></p><p dir="ltr"><b>RAO (Rao Optimization Algorithm):</b><br>RAO is a parameter-less optimization algorithm that updates solutions based on simple arithmetic operations involving the best and worst individuals in the population. …"