STATCOM_Ud

说明：  VC与matlab的混合编写程序的COM方法，如何调用COM进行混合编程。(Mixture of VC and matlab programming of COM methods, how to call COM mixed programming.)

matlab编写的黄金分割法求极值，C语言写鲍威尔修正算法，对n阶n次函数都实用，详细说明。(Matlab written in golden section method extremum, written in C language Powell correction algorithm, function of order n n practical details.)

多无人机协同决策与控制仿真平台（含论文教程和matlab代码），美国空军研究院开发，可用于学习无人机群体协同决策，以及算法验证，具有极高的价值(Collaborative decision-making and control of multiple UAVs simulation platform (including tutorials and papers matlab code), the U.S. Air Force Academy developed UAVs can be used to study collaborative decision-making groups, as well as algorithm verification, with a high value)

隐马尔科夫模型，matalb实现，比较好的(Hidden Markov Model, matalb achieved relatively good)

无线通信中微蜂窝和微微蜂窝信道的MATLAB建模(Propagation in Microcells and Picocells)

说明：   本书介绍了在Microsoft Windows 98、Microsoft Windows NT 4.0和Windows NT 5.0下程序写作的方法。这些程序用C语言编写并使用原始的Windows Application Programming Interface（API）。如在本章稍后所讨论的，这不是写作Windows程序的唯一方法。然而，无论最终您使用什么方式写作程序，了解Windows API都是非常重要的。(This book describes the Microsoft Windows 98, Microsoft Windows NT 4.0 and Windows NT 5.0 under the process writing approach. These procedures used C language and use the original Windows Application Programming Interface (API). As discussed later in this chapter, and it is not writing the only way to Windows program. However, regardless of how you use the writing process, understand the Windows API are very important.)

scatterbar就是画出三维散点柱状图，并且可以调整柱子的大小和颜色渐变，比原函数bar3更方便，更好用。(scatterbar is to draw three-dimensional scatter histogram, and can adjust the size and color gradients pillars, and more convenient than the original function bar3, easier to use.)

【matlab编程代做】matlab多边形相交 可以作为参考使用 学习([Do] matlab matlab programming generation lap maroon ball video can be used as a reference study)

this about the dynamic voltage restorer

说明：  包含一元线性回归分析、多元线性回归分析、逐步回归分析、非线性回归分析、主成分分析、因子分析、层次聚类分析、判别分析、自相关分析和自回归分析的全套Matlab源代码(MATLAB source code including linear regression analysis, multiple linear regression analysis, stepwise regression analysis, non-linear regression analysis, principal component analysis, factor analysis, hierarchical clustering analysis, discriminant analysis, autocorrelation analysis and autoregressive analysis)