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MATLAB Example Code : Non-Linear Least Squares --- Bearings-Only Measurement(MATLAB Example Code : Non-Linear Least Squares--- Bearings-Only Measurement)

说明：  有限元的四边形剖分程序，可用于有限元求解磁场，电场等问题是的区域划分中(fem use to answer EMT )

设有一长直矩形(a*2a)接地金属槽，上盖板对地绝缘ф1=10，侧壁，底ф2=0，求槽内的电位分布。用有限单元法求解。(Grounded metal tank with a long straight rectangular (a* 2a), on the cover insulation ф1 = 10, sidewalls, the bottom ф2 = 0, seeking a slot of the potential distribution. Using the finite element method to solve. )

QAM simulator for AWGN, multipath channel

4 files are included in the package: (1) fs01.m - the 1st-order local linear approximation (LLA) very similar to algorithm by Farmer and Sidorowich (1987). Uses constrained linear least squares (lsqlin) and (2) ts1.m - to reconstruct the vectors of the state space. (3) Example.m - runs fs01 and ts1 to predict (4) composite.txt - synthetic time series of decadal average sunspot number (4 files are included in the package: (1) fs01.m- the 1st-order local linear approximation (LLA) very similar to algorithm by Farmer and Sidorowich (1987). Uses constrained linear least squares (lsqlin) and (2) ts1.m- to reconstruct the vectors of the state space. (3) Example.m- runs fs01 and ts1 to predict (4) composite.txt- synthetic time series of decadal average sunspot number )

线性SVM算法设计分类器，对一组数据进行分类(Linear SVM classifier algorithm on a set of data classification)

图像立体匹配中区域匹配源码,实现了左右2副图像的区域匹配，得到了相应的视差图。(stero matching . region matching)

最小二乘辨识参数，用来辨识非线性系统的参数，还可以对非线性系统进行仿真。(Least squares identification parameters, used to identify nonlinear system parameters, it can also nonlinear system simulation. )

马氏距离是一种有效地计算两个样本集之间相似度的算法（数据之间协方差距离），与欧式距离相比，它考虑了各种特征之间的联系。本实验旨在通过给出的样本数据，设计一个最小马氏距离分类器并对测试点进行分类，然后将其与最小欧式距离分类器进行比较，实验得出当协方差矩阵为单位阵时，最小马氏距离分类器将与最小欧式距离分类器等价。本实验还根据实验样本设计贝叶斯分类器进行分类，并与最小马氏距离分类器进行比较，比较两种分类器的分类效果。(Markov distance is an effective method to compute the similarity between the two samples (data covariance distance), compared with the Euclidean distance, which takes into account the link between different characteristics. This experiment aimed at through the given sample data, design a minimum Mahalanobis distance classifier and classify the test points, and then compare it with the minimum Euclidean distance classifier. Experimental results showed that when the covariance matrix is a unit matrix, minimum Mahalanobis distance classifier with the minimum Euclidean distance classifier equivalent.In this experiment, according to the experimental design of the Bias classifier, and compared with the minimum distance classifier, the classification results of the two classifiers are compared.)

针对PSO算法优化之后的CLPSO算法，解决后期算法收敛速度问题等。(Aiming at the CLPSO algorithm after the optimization of PSO algorithm, the convergence speed of the later algorithm is solved.)