DINA模型及其参数估计，作者Jimmy de la Torre，发表于2009年，引用数为：385

This textbook evolved from a course in geophysical inverse methods taught during the past two decades at New Mexico Tech, first by Rick Aster and, subsequently, jointly between Rick Aster and Brian Borchers. The audience for the course has included a broad range of first- or second-year graduate students (and occasionally advanced under- graduates) from geophysics, hydrology, mathematics, astrophysics, and other disciplines. Cliff Thurber joined this collaboration during the production of the first edition and has taught a similar course at the University of Wisconsin-Madison. Our principal goal for this text is to promote fundamental understanding of param- eter estimation and inverse problem philosophy and methodology, specifically regarding such key issues as uncertainty, ill-posedness, regularization, bias, and resolution. We emphasize theoretical points with illustrative examples, and MATLAB codes that imple- ment these examples are provided on a companion website. Throughout the examples and exercises, a web icon indicates that there is additional material on the website. Exercises include a mix of applied and theoretical problems. This book has necessarily had to distill a tremendous body of mathematics and science going back to (at least) Newton and Gauss. We hope that it will continue to find a broad audience of students and professionals interested in the general problem of estimating physical models from data. Because this is an introductory text surveying a very broad field, we have not been able to go into great depth. However, each chapter has a “notes and further reading” section to help guide the reader to further explo- ration of specific topics. Where appropriate, we have also directly referenced research contributions to the field. Some advanced topics have been deliberately left out of this book because of space limitations and/or because we expect that many readers would not be sufficiently famil- iar with the required mathematics. For example, readers with a strong mathematical background may be surprised that we primarily consider inverse problems with discrete data and discretized models. By doing this we avoid much of the technical complexity of functional analysis. Some advanced applications and topics that we have omitted include inverse scattering problems, seismic diffraction tomography, wavelets, data assimilation, simulated annealing, and expectation maximization methods. We expect that readers of this book will have prior familiarity with calculus, dif- ferential equations, linear algebra, probability, and statistics at the undergraduate level. In our experience, many students can benefit from at least a review of these topics, and we commonly spend the first two to three weeks of the course reviewing material from

Inverse Problem Theory and Methods for Model Parameter Estimation （模型参数估计的反问题理论与方法） 作者：(意大利)(Albert Tarantola)塔兰托拉 PDF格式，英文。

Inverse problems arise when we reconstruct a sharper image from a blurred one or reconstruct the underground mass density from measurements of the gravity above the ground. When we solve an inverse problem, we compute the source that gives rise to some observed data using a mathematical model for the relation between the source and the data. This book gives an introduction to the practical treatment of inverse problems by means of numerical methods, with a focus on basic mathematical and computational aspects. To solve inverse problems, we demonstrate that insight about them goes hand in hand with algorithms. Discrete Inverse Problems: Insight and Algorithms includes a number of tutorial exercises that give the reader hands-on experience with the methods, difficulties, and challenges associated with the treatment of inverse problems. It also includes examples and figures that illustrate the theory and algorithms. Audience This book is written for graduate students, researchers, and professionals in engineering and other areas that depend on solving inverse problems with noisy data. The aim is to provide readers with enough background that they can solve simple inverse problems and read more advanced literature on the subject. Contents Preface; List of Symbols; Chapter 1: Introduction and Motivation; Chapter 2: Meet the Fredholm Integral Equation of the First Kind; Chapter 3: Getting to Business: Discretizations of Linear Inverse Problems; Chapter 4: Computational Aspects: Regularization Methods; Chapter 5: Getting Serious: Choosing the Regularization Parameter; Chapter 6: Toward Real-World Problems: Iterative Regularization; Chapter 7: Regularization Methods at Work: Solving Real Problems; Chapter 8: Beyond the 2-Norm: The Use of Discrete Smoothing Norms; Appendix A: Linear Algebra Stuff; Appendix B: Symmetric Toeplitz-Plus-Hankel Matrices and the DCT; Appendix C: Early Work on 揟ikhonov Regularization? Bibliography; Index. About the Author Per Christian Hansen is Professor of Scientific Computing at the Technical University of Denmark. His publications include two other books on inverse problems, several MATLAB?packages, and many papers on inverse problems, matrix computations, and signal processing. His home page is http://www2.imm.dtu.dk/~pch/. To request an examination copy or desk copy of this book, please use our online request form at www.siam.org/catalog/adopt.php.

ESPRIT-Estimation of Signal Parameters Via Rotational Invariance Techniques， author：RICHARD ROY AND THOMAS KAILATH, FELLOW，IEEE

postgres_opttune postgres_opttune是为调优PostgreSQL参数而开发的工具。 您可以自动找到适当的设置PostgreSQL参数。 下图显示了使用Oltpbenchmark的工作负载优化PostgreSQL 12的结果。 由...制作 ， ， 例子 以下过程用于在单个服务器上安装PostgreSQL12和postgres_opttune。 安装（pgbench） 使用pgbench进行调整时，请执行以下步骤。 python3安装 # yum install python3 python3-devel python3-libs python3-pip # pip3 install --upgrade pip setuptools 编译器（gcc）安装 # yum install gcc git安装 # yum install git Post

CEC2005是单目标测试集中比较常见的使用测试集，该资源经过测试，完全满足函数的表达式，各项数据文件都提供完整。CEC2005的文章链接如下：https://www.researchgate.net/publication/235710019_Problem_Definitions_and_Evaluation_Criteria_for_the_CEC_2005_Special_Session_on_Real-Parameter_Optimization

glcm matlab代码论文名称为“多点地统计的两种参数优化方法” 本文介绍了两种MPS参数优化方法的程序代码。 基于GLCM的方法的源代码是用MATLAB编写的，而深度学习程序代码是用c＃.Net编写的。 1.基于GLCM的方法主程序是“ GLCM_Method.m”，它取决于“ GrayCoMatrix.m”和“ HsimSimilarity.m”。 基于GLCM的方法中使用的第三方代码包含“ sort_nat.m”，“ rotateticklabel.m”。 2.基于深度学习的方法主程序是基于ML.Net的“ Program.cs”。该程序包含代码文件，例如“ Preprocessing_ImageFolder”，“ ImageNetData.cs”，“ MyDataTable.cs”等。 。 在使用这些程序之前，请解压缩两个压缩文件“ demo data.rar”和“ ML_Assets.rar”。 文件“ demo data.rar”包含了本文使用的数据，包括训练图像通道的实现以及基于Snesim和simpat获得的分类。

在此简要介绍中，通过使用边界控制方案解决了机器人飞机柔性机翼的控制问题。 受鸟类和蝙蝠的启发，具有柔韧性和铰接性的机翼被建模为由混合偏微分方程和常微分方程描述的分布式参数系统。 在原始耦合动力学上提出了机翼扭转和弯曲的边界控制，并通过引入适当的李雅普诺夫函数证明了边界稳定性。 仿真验证了所提出控制方法的有效性。

【jenkins】Extended Choice Parameter参数选择插件，方便自动化控制流程使用，根据预选的参数选择对应的自动化流程。