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一个修改的人工原生动物优化器,用于在非线性动态系统中强大的参数识别

Davut Izci1,2, Serdar Ekinci3, Gökhan Yüksek4

  • 1Department of Electrical and Electronic Engineering, Bursa Uludag University, Bursa 16059, Turkey.

Biomimetics (Basel, Switzerland)
|January 27, 2026
PubMed
概括

一个新的修改的人工原生动物优化器 (mAPO) 增强了复杂动态系统中的参数识别. 它平衡了全球搜索和本地改进,在非线性和混乱系统中提高了准确性和稳定性.

关键词:
纳尔德米德的简单方法.人工原生动物优化器优化器非线性系统是非线性系统.参数识别 参数识别随机学习机制是一种随机学习机制.

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