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一个基于内核极端学习机器的微地震信号预测模型,由哈里斯·霍克斯算法优化

Wei Zhu1, Yuting Bian1, Duo Lin1

  • 1School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.

Scientific reports
|November 18, 2025
PubMed
概括

这项研究引入了一种新的算法 (bKSHHO-KELM),用于使用微地震和爆炸信号早期检测岩石危险. 该方法实现了高精度,提高了资源开采期间的矿山安全.

关键词:
功能选择 功能选择全球优化全球优化哈里斯·霍克斯优化优化微观地震和爆炸信号.

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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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