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Updated: Jul 27, 2025

Author Spotlight: A Pseudotype Virus System for Assessing Omicron Subvariants and Neutralizing Antibodies in SARS-CoV-2 Research
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基于档案的冠状病毒群体免疫算法,用于优化神经网络中的权重.

Iyad Abu Doush1,2, Mohammed A Awadallah3,4, Mohammed Azmi Al-Betar5,6

  • 1College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait.

Neural computing & applications
|June 5, 2023
PubMed
概括

这项研究介绍了ACHIO,一种增强的冠状病毒群体免疫优化器 (CHIO),用于改善多层感知器 (MLP) 神经网络训练. ACHIO有效地优化了MLP参数,显著提高了不同数据集的分类准确性.

关键词:
档案技术 档案技术 档案技术奇约 奇约 奇约 奇约冠状病毒群体免疫优化器推进神经网络的Feedforward在MLP中,MLP是MLP.优化优化 优化优化

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科学领域:

  • 人工智能的人工智能
  • 机器学习 机器学习
  • 优化算法 优化算法

背景情况:

  • 对前神经网络的监督学习,特别是多层感知子 (MLP),严重依赖于权重和偏差的最佳配置.
  • 传统的MLP培训的梯度下降方法面临着局部最佳和缓慢的融合等挑战.
  • 在复杂的优化问题中,元启发式算法提供了一种替代方法来克服梯度下降的限制.

研究的目的:

  • 使用外部存档策略增强冠状病毒群体免疫优化器 (CHIO),创建 ACHIO 算法.
  • 应用增强的ACHIO算法来优化MLP神经网络的控制参数 (权重和偏差).
  • 评估ACHIO在提高MLP在各种数据集中的分类准确度方面的有效性.

主要方法:

  • 在CHIO算法中集成了一个外部存档策略,以引导人口在进化过程中朝着更有前途的搜索区域发展.
  • 改进的算法被称为ACHIO,用于训练MLP模型进行分类任务.
  • ACHIO的表现与六个已建立的群集智能算法和使用15个不同的分类数据集的原始CHIO进行了基准测试.

主要成果:

  • 拟议的ACHIO算法表现出卓越的性能,在十五个数据集中的十个数据集上,其分类准确度高于比较方法.
  • 在剩余的数据集上,ACHIO提供了极具竞争力的结果,表明其稳定性和有效性.
  • 在 ACHIO 中的外部存档策略成功地改善了 MLP 参数调整的优化过程.

结论:

  • 增强的CHIO算法 (ACHIO) 是优化MLP参数的强大工具,可以显著提高分类准确性.
  • ACHIO为训练神经网络的传统方法提供了一个有希望的替代方案,特别是在易于局部最佳化的场景中.
  • 整合外部存档策略是一种有效的技术,可以提高机器学习应用中的元启发算法性能.