基于粒子群采样的双精度数据融合建模
1. 西安交通大学, 数学与统计学院, 陕西西安 710049
2. 中国空气动力研究与发展中心, 计算空气动力研究所, 四川绵阳 621050
| 摘 要： | 计算机模拟和试验测试是工业设计中的常用的数据获取手段，因此综合使用以廉价计算数据为代表的低精度数据和以昂贵试验数据为代表的高精度数据进行融合建模是实际应用中的重要问题。为了提高模型的质量并控制高精度数据的使用量，本文提出了基于粒子群采样的双精度融合建模方法。算法使用Kriging模型对高低精度数据建模，并使用PSO（Particle swarm optimization）取低精度模型信息用于高精度建模。结合低精度数据数量充足，高精度数据昂贵、不易获取的特点，使用充分采样后建模的低精度模型指导高精度模型的初始采样，使用高低精度两个模型训练得到融合模型，并使用两个加点准则进行序贯建模，对融合模型进行持续优化。算法在一个标准函数和一个应用问题上进行了测试，实验结果显示在不提高高精度数据用量的前提下，算法改善了建模的质量，验证了提出算法的有效性。 |
| 关 键 词： | 双精度数据; 融合建模; 粒子群算法; 序贯试验设计; 克里金模型 |
| DOI： | 10.57237/j.wjms.2023.01.002 |
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收稿日期: 2023-03-27
接受日期: 2023-05-17
在线出版日期: 2023-05-25
Two-fidelity Data Fusion Modeling with Particle Swarm Sampling
1. School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an 710049, China
2. Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621050, China
| Abstract: | Computer simulation and experimental testing are commonly used means of data acquisition in industrial design, so the combination of low-precision data represented by cheap calculation data and high-precision data represented by expensive test data for fusion modeling is a hot topic in practical applications question. In order to improve the quality of the model and control the usage of high-precision data, this paper proposes a double-precision fusion modeling method based on particle swarm sampling. The algorithm uses the Kriging model to model high- and low-precision data, and uses PSO (Particle swarm optimization) to obtain low-precision model information for high-precision modeling. Combined with the characteristics of sufficient amount of low-precision data, high-precision data is expensive and difficult to obtain, the low-precision model modeled after sufficient sampling is used to guide the initial sampling of the high-precision model, and the fusion model is obtained by training with two high-precision and low-precision models, and two The point-adding criterion is used for sequential modeling, and the fusion model is continuously optimized. The algorithm is tested on a standard function and an application problem. The experimental results show that the algorithm improves the quality of the modeling without increasing the amount of high-precision data, which verifies the effectiveness of the proposed algorithm. |
| Keywords: | Two-fidelity Data; Fusion Modeling; Particle Swarm Optimization; Sequential Design of Experiments; Kriging Model |
基金
| 1. | 国家自然科学基金项目 (12171385) |
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