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H无穷鲁棒控制的一些实例与教程，其中包括程序文件以及pdf文件，适合初学者学习(H u65E0 u7A77 u9C01 u659 u659 u4E09 u4E09 u4E2 U53CApdf u6587 u4EF6 uFF0C u9002 u5206 u5216 u5B66 u8005 u5B66 u4E60)

是小学期课程设计的题目，包含特征值与特征向量的提取、训练样本以及最后的识别，多机电力系统仿真及其潮流计算。( Is the topic of the elementary school stage curriculum design, Contains the eigenvalue and eigenvector extraction, the training sample, and the final recognition, Multi-machine power system simulation and flow calculation.)

很不错的基础教程，英文版的，是文字排版，非扫描的，非常清晰。(Very good basic tutorial, the English version, text typesetting, non scanning, very clear.)

说明：  MFC Windows程序设计(第二版) 源代码，非书籍(MFC Windows Programming (2nd Edition) source code, non-books)

说明：  本书注重理论与实践相结合,通过实际案例和编程实现让读者理解理论在实践中的应用;同时还充分强 调“案例的实用性、程序的可模仿性”,且在案例程序中附有详细的注释。例如,投资组合管理、KMV模型计 算、期权定价模型与数值方法、风险价值VaR的计算等案例程序,读者可以直接使用或根据需要在源代码基 础上进行修改使用。(This book pays attention to the combination of theory and practice, through practical cases and programming to enable readers to understand the application of theory in practice; at the same time, it fully emphasizes "the practicality of cases, the imitatability of procedures", and has detailed notes in the case procedures. For example, case programs such as portfolio management, KMV model calculation, option pricing model and numerical method, VAR calculation can be used directly or modified on the basis of source code as required.)

说明：  C++ Primer第四版，光盘源码！如果你已经有电子书了，那赶快来下这个吧！(C++ Primer Fourth Edition, CD-ROM Source! If you already have e-books, then as soon as possible to the right under!)

说明：  c primer plus 中文版课本中代码(Codes in Chinese C Primer Plus)

说明：  实效编程百例,包含了用vc开发的各方面的源代码。(100 cases of the effectiveness of programming, including using vc development of all aspects of the source code.)

包括最小二乘法、SVM、神经网络、1_k近邻法,本科毕设要求参见标准测试模型，给出接收信号眼图及系统仿真误码率。( Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Undergraduate complete set requirements refer to the standard test models, The received signal is given eye and BER simulation systems.)

LMS算法实现一阶AR模型的线性预测估计(LMS algorithm for linear prediction estimation of first order AR model)