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Updated: May 29, 2025

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通过竞争性学习SSA (CL-SSA) 进行增强的极端学习机器,用于负载能力因子预测

Nuriddin Tahir S Luoka1, Wagdi M S Khalifa1

  • 1University of Mediterranean Karpasia, Turkey.

Heliyon
|February 3, 2025
PubMed
概括

本研究介绍了一种增强的竞争性学习跳跃群算法 (CLSSA),以提高极端学习机器 (ELM) 的性能. 通过CLSSA增强的ELM (ELM-CLSSA) 显著提高了对复杂环境因素的预测准确性.

关键词:
收精度 收精度 收精度极端学习培训 极端学习培训全球优化全球优化负载能力因子负载能力因子这就是SSA SSA.

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