搜索资源列表
hezha
- 此代码采用C++Ｂuilder 编写,提取断路器电流信号的特征，并计算相关参数！-this code using C Builder preparation, extraction circuit breaker current signal characteristics, and calculation of parameters!
pca
- PCA算法程序设计步骤： 1、去均值 2、计算协方差矩阵及其特征值和特征向量 3、计算协方差矩阵的特征值大于阈值的个数 4、降序排列特征值 5、去掉较小的特征值 6、去掉较大的特征值(一般没有这一步） 7、合并选择的特征值 8、选择相应的特征值和特征向量 9、计算白化矩阵 10、提取主分量
Principal-contens-analysis(PCA)
- 用于主成分分析（PCA）,包括，原变量相关系数矩阵的特征向量和特征值的求解，主成分的提取，载荷值的确定等-Used principal component analysis (PCA), including the original variable correlation matrix eigenvectors and eigenvalues of the solution, the main component of the extract, load value
cvfast
- fast-9特征点提取算法，源代码为图像特征点提取算法，特征点多，而且准确-fast-9 feature extraction
correlation-filter
- labview相关滤波，相关滤波提取特征频率下的原始信号-labview correlation filtering, correlation filter to extract the original signal characteristic frequency
mfcc
- matlab版本，提取语音梅尔频谱倒谱系数（MFCC）特征-Matlab version, Mel frequency cepstrum coefficient（MFCC） feature extraction of speech
Hearst-function-matlab-program
- 通过赫斯特函数 提取数据特征并画出曲线，可以直接运行-Characterized by Hearst function to extract data and draw the curve, you can directly run
approximate-entropy-matlab-program
- 通过近似熵函数 提取数据特征并画出曲线，可以直接运行-By approximate entropy feature extraction data and draw the curve, you can directly run
MFCC
- MFCC算法实例及源代码实现，用于提取语音特征进行进一步对比-Example of MFCC algorithm and Realization of the source code, for extracting speech features for further comparison
tfft
- 脉搏波频域参数提取，用于提取脉搏波的频域特征参数，提取后用于计算血压值或者血糖-Pulse wave frequency domain parameter extraction, used to extract the characteristic parameters of pulse wave frequency domain, after extracting the blood pressure value or blood sugar
feature_stacker
- feather_stacker主要用于一个数据集提取多个特征时，对特征进行联合的算法。-In many real-world examples, there are many ways to extract features a dataset. Often it is beneficial to combine several methods to obtain good performance. This example shows how to use FeatureUnion to c
principal-component-analysis
- 主成份分析程序，包含求相关系数矩阵/矩阵对角元素提取、创建对角阵、求特征值和特征向量等函数-Principal component analysis program, including seeking the correlation matrix/matrix diagonal elements extract, create diagonal, find eigenvalues and eigenvectors and other functions
gantiu_v21
- 包括随机梯度算法，相对梯度算法，二维声子晶体FDTD方法计算禁带宽度的例子,包含特征值与特征向量的提取、训练样本以及最后的识别。- Including stochastic gradient algorithm, the relative gradient algorithm, Dimensional phononic crystals FDTD method calculation examples band gap, Contains the eigenvalue and eigenvect
sai_re84
- 计算多重分形非趋势波动分析matlab程序,是学习PCA特征提取的很好的学习资料，比较了软阈值，硬阈值及当今各种阈值计算方法。- Calculation multifractal detrended fluctuation analysis matlab program, Is a good learning materials to learn PCA feature extraction, Comparison of soft threshold and hard threshold and
rf724
- 是学习PCA特征提取的很好的学习资料，FIR 底通和带通滤波器和IIR 底通和带通滤波器，包含光伏电池模块、MPPT模块、BOOST模块、逆变模块。- Is a good learning materials to learn PCA feature extraction, Bottom-pass and band-pass FIR and IIR filter bottom pass and band-pass filter, PV modules contain, MPPT module,
ersrb
- 是学习PCA特征提取的很好的学习资料，用MATLAB编写的遗传算法路径规划，使用起来非常方便。- Is a good learning materials to learn PCA feature extraction, Genetic algorithms using MATLAB path planning, Very convenient to use.
qui_v11
- 是学习PCA特征提取的很好的学习资料，代码里有很完整的注释和解释，FMCW调频连续波雷达的测距测角。- Is a good learning materials to learn PCA feature extraction, Code, there are very complete notes and explanations FMCW frequency modulated continuous wave radar range and angular measurements.
qing_v45
- sar图像去噪的几种新的方法，计算互信息非常有用的一组程序,是学习PCA特征提取的很好的学习资料。- Several new methods sar image denoising, Mutual information is useful to calculate a set of procedures, Is a good learning materials to learn PCA feature extraction.
bang-V5.8
- 包括单边带、双边带、载波抑制及四倍频,信号维数的估计，是学习PCA特征提取的很好的学习资料。- Including single sideband, double sideband, suppressed carrier and quadruple, Signal dimension estimates, Is a good learning materials to learn PCA feature extraction.
lei_xq73
- 用于信号特征提取、信号消噪，matlab程序运行时导入数据文件作为输入参数，利用最小二乘算法实现对三维平面的拟合。- For feature extraction, signal de-noising, Import data files as input parameters matlab program is running, Least-squares algorithm to fit a three-dimensional plane.