introduction-of-toolbox-of-fastICA

TE模特征方程的MATLAB图解TE模场分布的MATLAB图示(TE mode characteristic equation of the MATLAB graphical TE mode field distribution of the MATLAB icon)

轨迹绘制。点击鼠标左键确定轨迹典型点，点击右键结束绘制。(Trajectory drawn. Click the left mouse button to determine the trajectory of a typical point, right-click to end the drawing.)

PC码的BP解码,和传统的SC解码对比，有不错的优势(bp decoder for polar codes)

最小均方濾波器是一類可通過最小化誤差訊號之均方值而修正濾波器係數，以類比所需理想濾波器的自適應濾波器，其中作為修正依據的誤差訊號為理想參考訊號與實際輸出訊號之差。該種濾波器所用之最小均方法只以當前之訊號誤差值為準進行修正，是一種隨機梯度下降法。最小均方法係由斯坦福大學的Bernard Widrow教授及他的首位博士班學生Marcian Hoff於1960年提出。最小均方濾波器屬於常見的自適應濾波器種類之一。(The minimum mean square filter is an adaptive filter that can correct the filter coefficients by minimizing the mean square value of the error signal to analogize the desired ideal filter. The error signal used as the correction reference is the ideal reference signal and the actual output. The difference between the signals. The minimum averaging method used in this kind of filter is only corrected based on the current signal error value, which is a stochastic gradient descent method. The minimum mean method was proposed in 1960 by Professor Bernard Widrow of Stanford University and his first PhD student, Marcian Hoff. The least mean square filter is one of the common types of adaptive filters.)

半正定矩阵cholesky方法将其分解为两个上下三角矩阵相乘的形式。matlab自带的函数只能分解正定矩阵。并且给出例子。(Semi-definite matrix cholesky method be decomposed into two upper and lower triangular matrices form. matlab built-in function can only be positive definite matrix decomposition.)

说明：  用matlab编写的最小二乘算法。用matlab编写的最小二乘算法。(Matlab least squares algorithm)

ＡＲＩＭＡ　matlab实现　，　对时间序列进行预测分析(ARIMA matlab realize, prediction of time series analysis)

使用滞环比较器实现滞环控制的PWM整流器模型(Hysteresis comparator hysteresis-controlled PWM rectifier model)

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models.

卡尔曼滤波器 matlab实现(Kalman filter matlab implementation)