chapter7

该压缩包为matlab语言的常用算法程序集(The archive is commonly used algorithm matlab assembly language)

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)

cross entropy for function minimization

让某一频率以下的信号分量通过，而对该频率以上的信号分量大大抑制的电容、电感与电阻等器件的组合装置。 (low-pass filter )

用MATLAB编写的人脸识别算法，对PCA主成分分析进行了改进(Face recognition algorithm using MATLAB, the PCA principal component analysis has been improved)

神经网络LMS算法的学习规则，求的最大稳态学习速度，进行权值学习训练(artificial Neural Network Least Mean Square)

NLSE用于求解偏微分方程 可演示非线性光纤光学任何一个方程(NLSE pian weifen fang cheng qiu jie guang xian guang xue yan shi cheng xu ke jie ren he yi ge pain wei fen fang cheng )

Matlab basic course and implicit plot design

用泰勒级数展开的形式表示高动态的载波相位参数, 给出了对高动态载体和各阶频率参数 估计的四阶加权扩展卡尔漫滤波器(EKF) , 以及实现高动态跟踪滤波器必须的状态转移矩阵和动 态噪声协方差矩阵. 计算机模拟实验分析了对载波相位和各阶频率的跟踪结果.(Taylor series expansion with the form that the carrier phase high-dynamic parameters, given the high-order dynamic frequency carrier and the fourth-order parameter estimation Carl diffuse filter weighted extended (EKF), and to achieve the necessary high dynamic tracking filter state transition matrix and dynamic noise covariance matrix. The computer simulation analysis of carrier phase and frequency tracking results of each order.)

this is the simulation model of the two generator system with midpoint voltage compensation in the open loop using the fc-tcr with the firing of the scr in the open loop. the midpoint voltage iss compensated and bring down to the nearly 1 p.u. it is helpful to understand svc and also can be helpful to design in the closed loop control system for the compensataion... Arvind