HW1_2

Matlab的数理统计工具箱是Matlab工具箱中较为简单的一个，其牵扯的数学知识是大家都很熟悉的数理统计，因此在本文中，我们将不再对数理统计的知识进行重复，仅仅列出数理统计工具箱的一些函数，这些函数的意义都很明确，使用也很简单，为了进一步简明，本文也仅仅给出了函数的名称，没有列出函数的参数以及使用方法，大家只需简单的在Matlab工作空间中输入“help 函数名”，便可以得到这些函数详细的使用方法。(Mathematical Statistics Toolbox Matlab Matlab toolbox is a relatively simple one, it involves mathematical knowledge is the number of people are familiar with statistics, so in this article, we will no longer be on the knowledge of mathematical statistics to repeat, only lists Mathematical Statistics Toolbox functions that the meaning of these functions are very clear, very simple to use, in order to further condensed, this article only gives the name of the function, the parameters of the function is not listed, and the use of methods, we simply Enter the workspace in the Matlab " help function name" , it can be to use these functions in detail.)

采用matlab/simulink，简单通用电池模型及其参数测量(Using matlab/simulink, a simple generic model and its parameters measured battery)

本matlab程序用乘子法求解多元模型的最小值（给出初始点和精度要求条件）。(The matlab program for solving the minimum multivariate model using multiplier method (given initial point and precision conditions).)

悬臂梁振动主动控制，包括悬臂梁振动动力学模型，悬臂梁振动力学分析，悬臂梁振动微分方程，悬臂梁的PID控制仿真 (Cantilever vibration control, including the dynamic model of the cantilever vibration, the cantilever vibration mechanical analysis, differential equations cantilever vibration, the cantilever PID control simulation)

检测跟踪算法，检测算法用的kim算法，跟踪用的均值偏移算法(Detecting and tracking algorithm, detection algorithm method used by kim, tracking using mean shift algorithm)

用模拟退火求解非线性规划的具体例子，很有提高，希望大家能学习学习(Nonlinear programming using simulated annealing)

说明：  详细介绍了VC混合编程的内容，与MATLAB等的相互调用(Described in detail the content of VC Mixed Programming with MATLAB call each other, etc.)

qr法求特征值和特征向量 QR法能够避免矩阵奇异带来的错误，并能求解特征值有重根的情况(qr France seek eigenvalues and eigenvectors QR method can avoid the matrix singularity caused by errors, and can solve the eigenvalue of the double roots)

运用迭代法计算非线性方程组，并且编制程序对非线性方程组求解(Using the iterative method of nonlinear equations, and programming for solving nonlinear equations)

Locally-Linear Embedding (LLE)[9] was presented at approximately the same time as Isomap. It has several advantages over Isomap, including faster optimization when implemented to take advantage of sparse matrix algorithms, and better results with many problems. LLE also begins by finding a set of the nearest neighbors of each point. It then computes a set of weights for each point that best describe the point as a linear combination of its neighbors. Finally, it uses an eigenvector-based optimization technique to find the low-dimensional embedding of points, such that each point is still described with the same linear combination of its neighbors. LLE tends to handle non-uniform sample densities poorly because there is no fixed unit to prevent the weights from drifting as various regions differ in sample densities. LLE has no internal model.