NR_PowerFlow

《MATLAB仿真在现代通信中的应用》（徐明远）的随书光盘，里面附有全书的源码，很详细，很实用(" MATLAB simulation applications in modern communications" ( Xu Mingyuan ) of the book with CD-ROM, which attached to the source code, very detailed, very practical)

利用粒子滤波算法实现预测，算法可直接运行(particle filter prediction)

说明：  滑模控制，基于速度环的控制改进算法，滑模控制是变结构控制，又是非线性控制算法，应用于电机领域(Sliding mode control is an improved control algorithm based on speed loop. Sliding mode control is a variable structure control and a non-linear control algorithm, which is applied in the field of motor)

说明：  类的自定义 一些使用例子 很方便的教程实例 非常有用(Custom class that use a convenient example of an instance of a very useful tutorial)

无线传感器网络模糊功率控制源代码中的模糊控制模块 以及附有相应的图形显示(wireless sensor networks fuzzy power control of the source code control module and fuzzy with the corresponding graphics)

Extremely fast and lightweight WBXML parser, implementing only the subset of WBXML that EAS uses (as defined in the EAS specification).

用谱方法计算流体力学一些流动现象的整体稳定性,相控阵天线的方向图（切比雪夫加权），线性调频脉冲压缩的Matlab程序。( Spectral methods of computational fluid dynamics flow of some of the overall stability of the phenomenon, Phased array antenna pattern (Chebyshev weights), LFM pulse compression of the Matlab program.)

出一种实现不同维间估计结果自动配对的二维频率估计算法。首先把二维频率估计问题转化成两个矩阵束的特征值问题。根据矩阵束的特征求出两个矩阵束的公共特征向量，并以此为基础同时求出两个矩阵束的特征值。算法估计误差与现有算法相近，但解决了现有算法普遍存在的配对难题。 (Matrix pencils play an important role in numerical linear algebra. The problem of finding the eigenvalues of a pencil is called the generalized eigenvalue problem. The most popular algorithm for this task is the QZ algorithm, which is an implicit version of the QR algorithm to solve the associated eigenvalue problem B − 1Ax = λx without forming explicitly the matrix B − 1A (which could be impossible or ill-conditioned if B is singular or near-singular) )

排队论，讨论单窗口单队情形，两窗口单队情形。分别计算平均等待时间，平均逗留时间，平均每分钟服务的顾客人数。(Queuing theory to discuss the situation single-window single team, two windows one team situation. Calculate the average waiting time, average length of stay, the average number of customers per minute service.)

使用支持向量机SVM神经网络分类数据的预测(the prediction of data classification by SVM neural network)