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投影寻踪等级评级模型求解和遗传算法的实现(Projection Pursuit Model and Genetic Algorithm for Solving)

说明：  卡尔曼滤波器教程，matlab源码，每一步都有详细的说明，当作入门级教程很好(??????????????matlab?)

鲁棒主成分分析 低秩与稀疏矩阵分解 增广拉格朗日 图像重建、去噪(robust pca low-rank and sparse matrix decomposition)

一致性检测，判断矩阵，层次分析法，一致性(Conformance detection)

神经网络中的sofm算法的具体运用及程序实现，很有启发性(Sofm neural network algorithm and procedures used to achieve specific, very enlightening)

自己编的程序，自适应噪声对消语音增强，参考噪声与输入噪声是相关的情况。(Own procedures, Adaptive Noise Cancellation of Speech Enhancement, reference noise and input noise are relevant.)

非线性控制的描述函数法，非常经典的书籍 适合于入门学习(Nonlinear Control describing function method, a very classic books suitable for beginners to learn)

实现白噪声、正弦信号和带有白噪声的正弦信号的自相关函数的分析。(Achieve white noise, white noise with a sinusoidal signal and a sinusoidal signal of the autocorrelation function.)

设r是f(x) = 0的根，选取x0作为r初始近似值，过点（x0,f(x0)）做曲线y = f(x)的切线L，L的方程为y = f(x0)+f (x0)(x-x0)，求出L与x轴交点的横坐标 x1 = x0-f(x0)/f (x0)，称x1为r的一次近似值。过点（x1,f(x1)）做曲线y = f(x)的切线，并求该切线与x轴交点的横坐标 x2 = x1-f(x1)/f (x1)，称x2为r的二次近似值。重复以上过程，得r的近似值序列，其中x(n+1)=x(n)－f(x(n))/f (x(n))，称为r的n+1次近似值(Let r is f (x) = 0 root, select the initial approximation x0 as the r, over point (x0, f (x0)) to do the curve y = f (x) the tangent L, L the equation y = f ( x0)+ f ' (x0) (x-x0), find the intersection of L and the x-axis of abscissa x1 = x0-f (x0)/f' (x0), x1 is called an approximation r. Through points (x1, f (x1)) to do the curve y = f (x) the tangent, and find the intersection of the tangent with the x-axis of abscissa x2 = x1-f (x1)/f ' (x1), x2 is called r the second approximation. Repeat the process, get an approximation of the sequence r, where x (n+1) = x (n)-f (x (n))/f ' (x (n)), as an approximation of r n+1 times)

说明：  Simulation of Alamouti’s Scheme over Rayleigh fading channel. Assume perfect channel state information at the receiver. Plot BER performance (use gray mapping) and compare it to MRC with 2RX antennas. Compare the performance with MRC.