优化高斯过程回归 (GPR) 超参数,使用三个元启发算法来预测含有微封装PCM的悬浮物的粘度
Tao Hai1,2,3,4, Ali Basem5, As'ad Alizadeh6
1Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, China.
Scientific reports
|August 31, 2024
概括
用MXene颗粒预测微缩相变材料 (MPCM) 悬浮液的动态粘度对于热能储存至关重要. 由元启发算法优化的高斯过程回归准确地预测了这个属性,降低了实验室成本.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 热力学是一种热力学.
背景情况:
- 微封装相变材料 (MPCM) 对于热能存储 (TES) 系统至关重要.
- 应用范围包括建筑材料,织品和冷却技术.
- 精确预测动态粘度对于优化TES性能至关重要.
研究的目的:
- 准确预测MPCM和MXene颗粒悬浮液的动态粘度.
- 为了评估高斯过程回归 (GPR) 对此预测任务的有效性.
- 使用元启发式算法优化GPR超参数.
主要方法:
- 用高斯过程回归 (GPR) 来进行动态粘度预测.
- 分析了12个GPR超参数,并按重要性分类.
- 基因算法 (GA),粒子群优化 (PSO) 和海洋捕食者算法 (MPA) 用于超参数优化.
主要成果:
- 在所有测试的算法中,优化四个最重要的GPR超参数产生了大约0.9983的R值.
- 包括中度显著的超参数改进了模型,PSO达到0.99834.4的R值.
- 使用GA对所有12个超参数进行了全面优化,从而获得了最高的准确性 (R值为0.999224).
结论:
- 优化的GPR提供了一个非常准确的方法来预测MPCM和MXene悬浮物的动态粘度.
- 开发的模型为广泛的实验室测试提供了具有成本效益和效率的替代方案.
- 这种方法支持各种热能存储和管理系统的开发和优化.
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