Control_Brazo_Robotico1

基于mex的matlab-fortran混合编程，详细的步骤和样例(Mex-based hybrid of matlab-fortran programming, detailed steps and sample)

振动工具箱，源代码，有目录及详细说明，希望对大家有用(Vibration toolbox, source code, there are directory and detailed description, in the hope that useful)

转载的求lyapunov指数的程序，希望对大家有所帮助。(lyapunov exponent )

贝叶斯网络的matlab实现。可以创建贝叶斯网络、训练模型，以及实现统计推理。这里是最新的1.0.4版(Bayesian networks to achieve matlab. Bayesian networks can be created, the training model, and the achievement of statistical inference. Here is the latest version 1.0.4)

根据数据点拟合椭圆。。自己一直在用，很不错，是官网上下的(failed to translate)

最优化理论及其matlab设计中的armijo算法，用于最优化搜索算法的求解步长(Optimization theory and matlab design armijo algorithm for optimizing search algorithm for solving step)

在CR网络中，能量检测是最基础，也是最重要的一个环节，该程序实现了能量检测算法。(Energy detection of CR network)

说明：  利用矩量法求解Hallen方程 画出场的电流分布(Hallen method of moment equations current distribution appearances painting)

潮流计算仿真程序，包括所有IEEE标准节点系统(The trend of the computer simulation program, including all IEEE standard node system)

2. 设 是窄带信号，定义 是在 区间上均匀分布的随机相位。 是寬带信号，它是一个零均值、方差为1的白噪音信号e(n)激励一个线性滤波器而产生，其差分方程为 。 1） 计算 和 各自的自相关函数，并画出其函数图形。根据此选择合适的延时，以实现谱线增强。 2） 产生一个 序列。选择合适的 值。让 通过谱线增强器。画出输出信号 和误差信号e(n)的波形，并分别与 和 比较。 (Computer Experiments: 1. Consider an AR process x(n) defined by the difference equation where v(n) is an additive white noise of zero mean and variance .The AR parameters and are both real valued: a) Calculate the noise variance such that the AR process x(n) has unit variance .Hence , generate different realization of the process x (n). b) Given the input x (n), an LMS filter of length M = 2 is used to estimate the unknown AR parameters and . The step size is assigned the value 0.05. Compute and plot the ensemble average curve of and by averaging the value of parameters and over an ensemble of 100 different realization of the filter. Calculate the time constant according to the experiment results and compare with the corresponded theoretical value. c) For one realization of the LMS filter, compute the prediction error And the two tap-weight errors and Using power spectral plots of , show that)