formula4

这是一位在自适应鲁棒动态规划控制的牛人的的论文，对于学习自适应控制很有参考价值(This is a self-adaptive robust dynamic programming control of cattle people s papers, for learning adaptive control is very valuable)

关于使用vb学习ae的初级入门书籍，介绍了基本的源程序代码(Vb learning the ae primary introductory books, describes the basic source code)

说明：  优秀书籍，python入门，6-10章节的例子代码(Python book chapters 6-10 example source code)

ANSYS土木工程实例代码，是学习ANSYS软件的必备资料(ANSYS civil engineering example code, is to learn the necessary information ANSYS software)

非线性滤波的matlab实现；三维机动目标跟踪；(EKF)

一个好用的磁链观测模型文件，已经经过测试(A useful document flux model has been tested)

LMI 控制工具箱，采用内点法的 LMI 求解器，这些求解器比经典的凸优化算法速度有了显著提高。另方方面，它采用了有效的LMI结构化表示，在求解和计算领域做出了重大贡献。(The LMI control toolbox uses the LMI algorithm of the interior point method. These solvers have significantly improved the speed of the classical convex optimization algorithm. On the other hand, It uses effective LMI structured representation, and has made significant contributions in solving and computing fields.)

说明：  有关应用matlab编写的有限元函数,其中包含了一些最基本的有限元的基本函数,对于应用有限元法模拟电磁场欧很大的帮助(Matlab prepared for the application of the finite element function, which contains the most basic finite element basic function, using the finite element method simulation of electromagnetic fields in Europe a great help! !)

说明：  基于MATLAB建立HFSS的喇叭天线的模型(Modeling of Horn Antenna Based on MATLAB)

这本书在稀疏的多尺度图像和信号处理提出了艺术状态，包括线性多尺度变换，如小波，脊波和曲波变换、非线性、多尺度变换基于中值和数学形态学算子。最近的稀疏性和形态多样性的概念描述和利用各种问题，如去噪，反问题正规化，稀疏信号分解，盲源分离，压缩感知。 这本书的理论和实践研究相结合的领域，如天文学、生物学、物理学、数字媒体应用和取证。最后一章探讨了信号处理中的一个范式转换，表明以前的信息取样和提取的限制可以用非常重要的方法加以克服。 MATLAB和IDL代码伴随这些方法和应用程序重现。 实验并说明了在相关网站上可下载的研究的推理和方法。(This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Recent concepts of sparsity and morphological diversity are described and exploited for various problems such as denoising, inverse problem regularization, sparse signal decomposition, blind source separation, and compressed sensing. This book weds theory and practice in examining applications in areas such as astronomy, biology, physics, digital media, and forensics. A final chapter explores a paradigm shift in signal processing, showing that previous limits to information sampling and extraction can be overcome in very significant ways.)