基于遗传算法和莱文伯格-马奎特优化算法的2S2P1D模型和哈夫里利亚克-内加米模型的参数确定
Mingzhu Qiu1, Peng Cao1, Liang Cao1
1Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100084, China.
Polymers
|June 10, 2023
概括
这项研究优化了粘弹性模型参数,使用遗传算法 (GA) 和Levenberg-Marquardt (L-M). GA提高了2S2P1D和Havriliak-Negami模型的适配精度,实现了高的相关系数.
科学领域:
- 材料科学 材料科学 材料科学
- 计算建模 计算建模
- 类风病学 类风病学 类风病学
背景情况:
- 粘弹性模型描述了材料在压力下的行为.
- 准确的参数获取对于模型验证至关重要.
- 常见的模型包括2S2P1D和Havriliak-Negami (H-N). 这两种模型是最常见的.
研究的目的:
- 为了优化2S2P1D和H-N粘弹性模型的参数采集.
- 研究遗传算法 (GA) 和莱文伯格-马奎特 (L-M) 算法的联合效应.
- 分析GA在不同粘弹性模型中的适用性.
主要方法:
- 使用遗传算法 (GA) 和Levenberg-Marquardt (L-M) 进行参数优化.
- 将GA和L-M应用于2S2P1D和H-N组成方程.
- 为H-N模型开发了一种改进的半分析方法,涉及Cole-Cole曲线的拟合.
主要成果:
- GA为2S2P1D模型保证了0.99的相关系数,通过L-M二次优化进行了改进.
- 改进的半分析方法实现了H-N模型的>0.98相关性.
- H-N模型优化显示了对实验数据离散性和重叠性的敏感性.
结论:
- 对于优化粘弹性模型参数,特别是对于2S2P1D模型,GA是有效的.
- 拟议的半分析方法提高了H-N模型的适配精度.
- 在H-N模型中,分数功率函数影响优化结果.
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