PCA-tutorial

matlab算法计算的源代码,希望对学matlab的同学有所帮助(algorithm matlab source code, hope to help students learn matlab)

Hapke2002年模型仿真，以及模型参数的反演。(Hapke model implementation)

Modelling moto DC controller

其中的相应程序为实现解决线性方程组的相应的方法，程序测试正确。(Corresponding procedures for the realization of the appropriate method to solve linear equations, the correct program testing.)

此示意程序用DWT实现二维小波变换 1.调原始图像矩阵load wbarb 2.进行二维小波分解l=wfilters( db10 , l ) 3.分解结果显示figure(1) (This indicated procedures used to achieve two-dimensional wavelet transform DWT 1. Emphasized the original image matrix load wbarb 2. For two-dimensional wavelet decomposition l = wfilters (' db10' , ' l' ) 3. Decomposition results figure (1) )

maze solution. it is a solution for maze problem with matlab code. in this solution I used very simple functions and it is easy for everyone.

智能控制（刘金琨版）第8章第1题课后练习MATLAB程序(Intelligent Control (Hangzhou Press Edition) Chapter 8 Question 1 Homework MATLAB program)

　MUSIC 算法是利用接收数据的协方差矩阵(Rx)分离出信号子空间和噪声子空间，利用信号方向向量与噪声子空间的正交性来构成空间扫描谱，进行全域搜索谱峰，从而实现信号的参数估计。(MUSIC algorithm is used to receive data covariance matrix (Rx) to isolate the signal subspace and noise subspace, using the signal direction vector and noise subspace orthogonality to form the spatial scan spectrum, full-domain search peaks in order to achieve signal The parameter estimation.)

理解随机信号功率谱分析原理和方法。 (1) 生成信号，被淹没在在噪声中； (2) 试用周期图法估计信号的功率谱； (3) 选用不同窗，使用修正周期图法估计信号的功率谱； (4) 对一段语音信号，使用LD算法估计其功率谱； (5) 详细列出功率谱估计的步骤和原理。 (understanding of random signal power spectrum analysis principles and methods. (1) generate signals being drowned in the noise; (2) The probationary period plan estimates the signal power spectrum; (3), different windows, the use of map amendment cycle method to estimate the power spectrum signal; (4) a voice signal, the use of LD algorithm to estimate the gong spectrum; (5) a detailed breakdown of the power spectrum estimation steps and principle.)

实现有效的学习算法 的稀疏贝叶斯模型，即稀疏贝叶斯matlab工具箱("SparseBayes" is a package of Matlab functions designed to implement an efficient learning algorithm for "Sparse Bayesian" models. The "Version 2" package is an expanded implementation of the algorithm detailed in: Tipping, M. E. and A. C. Faul (2003). "Fast marginal likelihood maximisation for sparse Bayesian models." In C. M. Bishop and B. J. Frey (Eds.), Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6. This paper, the accompanying code, and further information regarding Sparse Bayesian models)