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Updated: Jun 19, 2025

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数学建模和基于机器学习的优化,以提高挥发性有机化合物的生物过效率.

Muhammad Sulaiman1, Osamah Ibrahim Khalaf2, Naveed Ahmad Khan3,4

  • 1Department of Mathematics, Abdul Wali Khan University, 23200, Mardan, Pakistan.

Scientific reports
|July 23, 2024
PubMed
概括

这项研究使用Elman神经网络 (ENN) 和Levenberg-Marquardt (LM) 优化模型对挥发性有机化合物 (VOC) 的生物过. 该ENN-LM方法准确地预测生物膜中的甲醇和p-pinene和度,为化学工程应用提供了强大的工具.

关键词:
人工智能的人工智能埃尔曼神经网络是一个神经网络.数学建模的数学建模迈凯利斯-门运动学优化优化 优化优化反应机制的反应机制有监督的机器学习.挥发性有机化合物 挥发性有机化合物

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