Lesson1

这是清华版c＋＋程序设计 第四章课后题第10题(This is the Tsinghua version c Program Design Chapter IV title after title No. 10)

介绍了MathType的一些使用技巧，包括公式输入，公式排版等，给出了详细的图文教程(MathType introduced some tips, including formulas enter the formula typesetting, gives a detailed graphic tutorials)

本文主要内容是关于单相PWM逆变器的输出电压波形控制技术。好的输出波形 包含两个方面的要求：稳态精度高和动态响应快。本文围绕这两点，分析了重复控制 技术和双环控制技术，并进行了一系列仿真和实验。 (The output waveform correction technology of single-phase PWM inverter is studied in this dissertation.High quality waveform has two characteristics.It has not only nice static characteristic but also well dynamic response.Aiming at these characteristics, repetitive control technology and dual-loop control technology is analyzed.Both these control scheme are verified by simulation and experiment. )

包含c语言的所有库函数，并附有实例，非常实用的程序员手册。(C language contains all the library functions, together with examples, very practical programmers manual.)

ubuntu系统性能优化简单教程 关闭最小化动画 长期使用 Ubuntu 后有一种感觉，那就是在 GNOME 中启动应用程序时，速度越来越慢。在 Ubuntu 英文论坛那边看到一个技巧，可以对这个问题起到改善作用。打开 /etc/hosts 文件，可以看到类似下面的内容： 。。。。。。。。。。。。(Ubuntu system performance optimization simple tutorial Close minimize animation After long-term use of Ubuntu, it is a kind of feeling in the GNOME started in the application, the more and more slow. In English BBS there see a Ubuntu on this problem technique that can play improved. Open/etc/hosts file, can see similar the following content: )

VF上课使用的ppt课件，内容较全，欢迎下载使用，哈哈。(VF ppt Courseware)

fichier de guide de demmarage Altuim designer

关于SDRAM的文件为了下载传上来的 这回够20字可以下了吗(About the pass file to download to the SDRAM)

ansys作为一款多物理场有限元仿真软件，深受大家喜爱，文章中包含了一些ansys使用过程中的注意事项，对ansys的学习很有帮助。(ansys as a multi-physics finite element simulation software, beloved, the article contains some ansys precautions during use of ansys very helpful.)

关于关联向量机应用的最新文献！ 提出了一种核主元分析(KPCA)和关联向量机(RVM)相结合的组合建模方法。KPCA-RVM采用KPCA对原始自变量进行非线性变换并提取主成分,形成特征自变量 采用RVM,对KPCA变换后的样本数据进行回归建模,并根据模型的预报能力自适应的确定参与回归的最佳特征变量个数,消除冗余信息干扰,获得强非线性表达能力且预报性能良好的模型。并将KPCA-RVM应用于PTA装置对羧基苯甲醛(4-CBA)含量的软测量建模,结果表明该方法预测精度高于PCA-RVM和RVM。(A novel modeling method integrated KPCA with RVM was proposed. The kernel primary component analysis (KPCA) was employed to identify the principal components from the nonlinear transform data of independent variables, which were regarded as character variables. Regression between character variables and dependent variables was done based on RVM, and the optimal number of the character variables was adaptively determined according to the generalization performance of the regression model. Thus, KPCA-RVM meth...)