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所在平台: Udemy |
课程主页: https://www.udemy.com/course/design-of-experiments-for-optimisation/
课程评论：没有评论
## 课程名称：实验设计优化 **课程概述：** 本课程“实验设计优化”旨在教授学员如何通过科学的实验设计来最大化或最小化响应变量。实验在科学研究、技术开发、产品设计与配方、商业化以及工艺改进等众多领域中扮演着至关重要的角色。一个精心设计的实验是获得可信结果和准确结论的基础，因为实验数据的收集方式直接影响了可以从中得出的结论。 **先修知识：** 本课程并非入门级别，需要学员具备一定的实验设计基础概念，例如方差分析（ANOVA）和析因设计（Factorial Designs）。这些知识可以在“实验设计与分析”课程或其他相关资源中找到。 **课程内容：** 1. **线性回归模型简介：** * 学习如何构建回归模型来拟合实验数据。 * 掌握模型拟合的评估方法（模型充分性检验）。 2. **线性模型实验设计：** * 探讨用于线性模型的实验设计。 * 学习使用中心点（Central Points）来检验模型的线性（失拟检验）。 * 应用线性模型处理具有不精确水平的因子和缺失观测值的实验。 3. **响应面方法（Response Surface Methodology, RSM）：** * **最速上升法（Path of Steepest Ascent）：** 从析因设计开始，通过拟合线性模型来寻找响应最快的增长方向。 * **中心复合设计（Central Composite Design, CCD）：** * 学习使用中心复合设计拟合二次模型。 * 确定能够最大化响应的实验条件。 * 通过两个实例，学习如何同时分析多个响应。 * **三水平设计：** * **Box-Behnken 设计：** 学习如何使用该设计。 * **面心复合设计（Face-Centered Composite Designs）：** 学习如何使用该设计。 **实际应用与工具：** * 课程中的所有案例均来源于工业界和学术界的研究实例。 * 数据分析将主要使用 **R-Studio** 进行。尽管这不是一门 R 语言课程，但即使不熟悉 R 的学员也能从中受益。课程提供的 R 代码和数据文件可以下载，函数会进行简要说明，学员可以方便地将其改编用于分析自己的数据。 * 若学员已熟悉其他实验设计软件，也可下载数据并使用自己选择的软件进行复现分析，结果将完全一致。 **目标学员：** 任何进行实验的人员，特别是工业界和学术界的研究人员、硕士和博士研究生以及工程师。
Welcome to "Design of Experiments for Optimisation"!Experimentation plays an important role in science, technology, product design and formulation, commercialization, and process improvement. A well-designed experiment is essential once the results and conclusions that can be drawn from the experiment depend on the way the data is collected.This course will cover the basic concepts behind the Response Surface Methodology and Experimental Designs for maximising or minimising response variables.This is not a beginner course, so to get the most of it, you need to be familiar with some basic concepts underlying the design of experiments, such as analysis of variance and factorial designs.You can find it in my course "Design and Analysis of Experiments" or on several other courses and resources on the market.The course starts with a basic introduction to linear regression models and how to build regression models to fit experimental data and check the model adequacy. The next section covers experimental designs for linear models and the use of central points to check the model's linearity (lack-of-fit). By the end of the section, we will be using linear models to fit experiments with inaccurate levels in the design factors and missing observations.By then, we will be ready for Response Surface Methodology. We will start with a factorial design to fit a linear model and find the path of the steepest ascent. And then, we are going to use a central composite design to fit a quadratic model and find the experimental conditions that maximise the response. Moreover, we will see how to analyse several responses simultaneously using two very illustrative and broad examples.Finally, we will see how to use three-level designs: Box-Behnken and face-centred composite designs.The whole learning process is illustrated with real examples from research in the industry and in the academy.The analysis of the data will be performed using R-Studio. Although this is not an R course, even students who are not familiar with R can enrol in it. The R codes and the data files used in the course can be downloaded, the functions will be briefly explained, and the codes can be easily adapted to analyse the student's own data.However, if you are already familiar with using other DoE software, feel free to download the data and reproduce the analysis using the software of your choice. The results will be exactly the same.Any person who performs experiments can benefit from this course, mainly researchers from the academy and the industry, Master and PhD students and engineers.
