使用元启发式算法对神经网络模型的优化,用于估计选定岩石类型的动态波桑比率
Umer Waqas1, Muhammad Farooq Ahmed2, Hafiz Muhammad Awais Rashid2
1Department of Geological Engineering, University of Engineering and Technology, Lahore, 54890, Pakistan. umerwaqas@uet.edu.pk.
这项研究优化了神经网络模型,用于预测岩石的动态特性. 一个粒子群集优化器显著提高了预测准确性,实现了动态波桑波桑的R2为0.954,达到0.954的R2.
科学领域:
- 地质物理学 地质物理学
- 材料科学 材料科学 材料科学
- 计算机建模 计算建模
背景情况:
- 了解岩石的动态特性对于各种工程应用至关重要.
- 这些属性的预测建模可以增强分析和决策.
- 神经网络为复杂的财产估计提供了强大的工具.
研究的目的:
- 开发和优化用于预测岩石动态性质的神经网络模型.
- 评估不同反向传播神经网络架构的有效性.
- 使用元启发式优化技术来增强模型性能.
主要方法:
- 测量岩石的动态特性,包括质量因子 (Q),共振频率 (FR),声阻抗 (Z),振荡衰变因子 (α) 和动态波桑比率 (v).
- 开发了15个反向传播神经网络模型 (前,级联前,Elman).
- 使用粒子群优化器优化表现最好的模型.
主要成果:
- 40个神经元的前神经网络模型显示出最好的初始性能 (R2 = 0.797).
- 使用粒子群优化器进行的优化显著提高了确定系数,使得R2 = 0.954.
- 岩石硬度因裂变形和微裂发展而随着激发频率而变化.
结论:
- 像粒子群优化一样,元启发式算法对于提高预测模型质量是有效的.
- 优化模型可以可靠地用于数据建模,模式识别和分类任务.
- 这种方法为解决复杂的地质力学和材料科学问题提供了宝贵的参考.
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