shaped-Penny-crack-code

自己编的matlab程序。用于模式识别。结合特征提取方法sequential_forward_selection使用。(The matlab own procedures. For Pattern Recognition. Combination of feature extraction methods sequential_forward_selection use.)

This is a matlab code for processing an image by selecting non-overlapping blocks. This code also uses built in functions in matlab

说明：  正交匹配追踪(OMP)算法的MATLAB函数代码(Matlab function code of OMP algorithm)

讲基于q学习的应用的相关文献，不错的学习例子(Q learning about the application based on the literature, examples of good learning)

切削力建模,仿真以及优化依据Altintas machining automation(cutting force modeling)

PCA algorithm for BSS.

说明：  该算法为基于sugeno的倒立摆模糊控制 用MATlab开发，能够管直接运行(The algorithm for the Sugeno-based fuzzy control of inverted pendulum using Matlab development, be able to pipe directly to run)

该文件是模拟电磁波传播过程中遇到障碍物遮挡使信号与接收装置之间不能直视而产生的传输误差，对模拟电磁波传播具有很大帮助。(The file is a simulation of the transmission error caused by the obstacle occlusion in the process of electromagnetic wave propagation, which can not be seen directly between the signal and the receiving device. It is of great help to the simulation of electromagnetic wave propagation.)

说明：  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)

基于分数低阶协方差仿真的时延估计问题，其基本思想是利用两个接收信号的相关函数来估计时间延迟。(Based on the scores of low-level simulation of time delay covariance estimation problem, the basic idea is to use of the two received signal correlation function to estimate the time delay.)