Arnold

IMM算法仿真,Matlab源码,用了最小二乘法，EKF，UFK等几种方法比较(IMM algorithm simulation, Matlab source code, using the least squares method, EKF, UFK Comparison of several methods such as)

VSRS虚拟视点合成软件。完整版。导入到VS中，重新编译就可以使用。(VSRS virtual viewpoint synthesis software. The full version. Imported into the VS, and recompile it can be used)

说明：  software engg notes for students

说明：  1.这是一个递推极大似然算法代码; 2. 附一份PDF说明文件.(1.This is a recursive maximum likelihood mehod code; 2.There is the PDFfile.)

文档包括matlab应用于统计分析的40个案例，比较经典(matlab statatic anlysis)

利用matlab设计FIR低通滤波器和IIR低通滤波器。编译通过。(matlablowpass filter)

Eigenfaces: PCA tends to find a p-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space (p  N). We called the new subspace defined by basis vectors “face space”. First, all training faces are projected onto the face space to find a set of weights that describes the contribution of each vector. Then we project all testing faces onto the face space to obtain a set of weights. Finally, we identify the face by comparing a set of weights for the testing face to sets of weights of training faces.

说明：  微分进化算法，用于参数优化、寻找极值等，可自行加入其他约束条件和修改目标函数(Differential evolution algorithm for parameter optimization, search extreme, can free to add other constraints and modify the objective function)

文献1的仿真程序 solves the linear least squares problem with nonnegative variables = using the newton s algorithm in [1]. [1] Portugal, Judice and Vicente, A comparison of block pivoting and ％ interior point algorithms for linear least squares problems with ％ nonnegative variables, Mathematics of Computation, 63(1994), pp. = 625-643(literature of a simulation program solves the linear least squares p roblem with nonnegative variables using the n = ewton's algorithm in [1]. [1] Portugal. Judice and Vicente. A comparison of block pivoting and interior% po int algorithms for linear least squares proble ms% with nonnegative variables, Mathematics of Computation, 63 (1994), pp. = 625-643)

This is a Matlab code explaining the Primal SVM training and testing algorithm using Lagrange mathematical theory