Statistical-Analysis

Prony算法工具箱。Prony方法是用一组指数项的线性组合来拟和等间距采样数据的方法,可以从中分析出信号的幅值、相位、阻尼因子、频率等信息。considerations including data preprocessing, model order selection, model order selection criteria, signal subspace selection, signal and noise separation, root inspection and assessing residuals. The PTbox provides flexibility to compare and display analysis results simultaneously for several parameter variations.(Prony algorithm toolbox. Prony method is used for a group of items to be linear combination of distance and other data sampling methods be able to analyze the signal amplitude, phase, the damping factor, frequency, and other information. Considerations including data preprocessing , model order selection, model order selection criteria. signal subspace selection, signal and noise separation, root inspection and assessing residuals. The P Tbox provides flexibility to compare and displ ay analysis results simultaneously for severa l parameter variations.)

用于比较运动捕捉数据降维后提取的极值点之间的运动类型比较(For comparing the motion capture data movement type drop take extreme points of comparison between)

大学物理光学实验中经典的迈克尔逊干涉实验源码，有友好的交互界面，简单易操作。(University of Physical Optics classical Michelson interferometer experiment experimental source, a friendly interface, easy to operate.)

混沌时间序列加权零阶局域预测.对混沌时间序列进行零阶加权计算，是混沌时间序列预测方法之一(zero-order Weighted partial method)

最小二乘回归分析算法，是机器学习的例程，电力系统暂态稳定程序，可以进行暂态稳定计算。( Least-squares regression analysis algorithm, Machine learning routines, Power System Transient Stability Program, can be transient stability.)

说明：  在微电网的优化配置过程中采用遗传算法对其进行优化配置(Genetic algorithm is used to optimize the microgrid configuration)

说明：  求解三维装箱问题的启发式算法，利用Matlab语言进行实现，包含所有m函数名文件(seek/find the solution (of a mathematical problem) heuristic approach)

神经网络中的对象传播神经网络CPN的学习算法(Neural network objects CPN-propagation neural network learning algorithm)

说明：  遗传工具箱类构造函数。分为染色体类构造函数。一般基类的构造根 ，对于连续型设计变量的数据提取的构造。得到的染色体设置属性(Genetic toolbox class constructor. Divided into the chromosome class constructor. The general structure of the root base class for the continuous design variables of the structure of data extraction. Have set the properties of chromosomes)

The local binary pattern (LBP) is a simple yet very efficient operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. Perhaps the most important property of the LBP operator in real-world applications is its invariance against monotonic gray level changes caused, for example, by illumination variations. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. LBP is also very flexible: it can be easily adapted to different types of problems and used together with other image descriptors