论文标题
工程实验的自适应随机实验设计方法
An adaptive random experiment design method for engineering experiment
论文作者
论文摘要
本文提出了一种自适应随机实验设计（ARED）算法，可用于优化多个因素和水平实验。该算法将实时模型误差作为自适应条件，并输出一个符合基于自动过程的误差量化标准的模型。根据实际的实验场景，在双峰高斯函数，双峰表面函数和峰功能之间选择了相似数量的测试用例和比较实验设计方法。同时，支持向量机（SVM）算法用于为所选测试用例构建模型，并预测验证表面（或曲线）。定性和定量分析以适用性和精度的两板板进行。结果表明，ARED方法可以应用于多因素的实验，并且比比较实验方法具有更好的精度和适用性。
This paper proposes an adaptive random experiment design (ARED) algorithm that can be applied to optimize the multiple factors and levels experiments. The algorithm takes real-time model error as the adaptive condition, and outputs a model that conforms to the error quantization standard based on the automatic process. According to the actual experimental scenario, the similar number of test cases were selected between the ARED method and the comparative experimental design method under the bimodal Gaussian function, the bimodal surface function and the peaks function, respectively. simultaneously, the support vector machine (SVM) algorithm is used to construct the model for the selected test cases, and the verification surface (or curve) is predicted. The qualitative and quantitative analysis is carried out at two-slice of applicability and precision. The results show that the ARED method can be applied to the experiment of multi-factor, and has better precision and applicability than the comparative experimental methods.
