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hough transform this project applyes hough transform on given picture in order to be able to calculate object in real world

说明：  本程序采用matlab求解常见的几种偏微分方程(This procedure uses matlab partial differential equations to solve several common)

Lagrange插值多项式求线性方程组,数值分析考试用(Lagrange polynomial interpolation of linear equations, numerical analysis examinations)

video tracking mean shift implementation

基于Elman神经网络的电力负荷预测模型研究(Research on power load forecasting model based on Elman neural network)

K最邻近密度估计技术是一种分类方法，不是聚类方法。 不是最优方法，实践中比较流行。 通俗但不一定易懂的规则是： 1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。 2.选出最小的前K数据个距离，这里用到选择排序法。 3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method. It is not the best method, but it is popular in practice. Popular but not necessarily understandable rule is: 1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov). 2. select the minimum distance from the previous K data, where the choice sorting method is used. 3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)

利用极大值估计法进行节点之间的定位。模拟真实环境进行仿真( Estimated using the maximum method of positioning between nodes. Simulate the real environment simulation)

说明：  matlab开发函数，一些简单的例子和数字滤波器设计函数(matlab development function, some simple examples and digital filter design function)

原始对偶内点算法,可以解决最优化问题。程序已经调试验证过，OK。(primary-dual interior method)

输入信号为方波信号+高斯白噪声，方波信号基 频为1kHz，幅值为1，高斯白噪声方差为0.2， 均值为0，采样频率为20kHz。试用周期图法和 ARMA谱估计法(SVD-TLS)估计此信号的功率 谱，并分析比较其结果(The input signal is a square wave signal+ Gaussian white noise, square wave signal fundamental frequency 1kHz, amplitude is a Gaussian white noise variance is 0.2, the mean is 0, the sampling frequency is 20kHz. Try periodogram and ARMA spectral estimation method (SVD-TLS) estimate the power spectrum of this signal, and the analysis and comparison of the results)