toushi

本文来自卡尔曼发表的一篇论文，分析维纳滤波不足的基础上，发表的最优估计算法(This article from the Kalman published a paper, analysis based on the lack of Wiener filtering, published in the optimal estimation algorithm)

simulink应用实例，基于MATLAB6.5系列(Simulink application examples, based on a series of MATLAB6.5)

you can plot the Array function of Smart Antenns

一个基于BP神经网络预测的matlab小程序，可以参考一下(based on an artificial neural network to predict the Matlab small procedures that will make reference to)

用MATLAB实现神经网络BP算法。思路比较清晰，大家可以作为参考。(MATLAB neural network with BP algorithm to achieve. Ideas clearer, we can serve as a reference.)

创建一个n维的稀疏数组对象，n是任意值。 定义N可能是大于2的一类n维稀疏阵列。然而，它应该被认为是一种起动方式与普通的MATLAB稀疏矩阵和重塑它有N维度。换句话说，稀疏的数据，首先必须能够作为一个普通的2D MATLAB稀疏矩阵被前n维。事实上，如果目标数组的尺寸mxnxp……yxz，然后将它存储在内部类是一个普通的二维稀疏阵列的尺寸（M×N×P×……×Y）XZ。这导致了某些内存株时使用大量的尺寸。我发现有用的类主要用于中等尺寸像三维图像边缘检测，你经常要举行一个稀疏的3D的边缘地图。(Creates an N-dimensional sparse array object, for arbitrary N.This submission defines a class of N-dimensional sparse arrays for N possibly greater than 2. However, it should really be thought of as a way of starting with an ordinary MATLAB sparse matrix and reshaping it to have N dimensions. In other words, the sparse data must first be able to exist as an ordinary 2D MATLAB sparse matrix before being made N-dimensional. In fact, if the intended array has dimensions MxNxP...YxZ, then the class will store it internally as an ordinary 2D sparse array of dimensions (M*N*P*...*Y)xZ. This leads to certain memory strains when using large numbers of dimensions. I find the class useful mainly for moderate dimensional things like edge detection in 3D imaging, where you often want to hold a sparse 3D edge map. )

大地坐标变换，转换经度很高，修改正了反变换有0.6误差的错误。下载即可使用。(Geodetic coordinate transformation, the conversion of longitude is very high, change the inverse transform has 0.6 error. You can use to download. )

用分布傅里叶计算硅波导中的非线性薛定谔方程，考虑硅波导中色散及非线性损耗(solution of NLSE(two pulses propagate in silicon waveguide) using split step fourier method)

基于内容的图像识别-支持向量机方法-8分类(Content-based image recognition- classification of support vector machines-8)

自己写的Possion过程仿真，包括Possion过程的产生和检验。用指数分布叠加产生，用kstest函数检验。(Write your own Possion process simulation, including the production and testing Possion process. Produced by superposition of exponential distribution, with kstest function test.)