搜索资源列表
siemai
- 是学习PCA特征提取的很好的学习资料，包括压缩比、运行时间和计算复原图像的峰值信噪比,通过matlab代码。- Is a good learning materials to learn PCA feature extraction, Including compression ratio, image restoration computing uptime and peak signal to noise ratio, By matlab code.
giuyan
- 验证可用，是学习PCA特征提取的很好的学习资料，数据模型归一化，模态振动。- Verification is available, Is a good learning materials to learn PCA feature extraction, Normalized data model, modal vibration.
nounun
- gmcalab 快速广义的形态分量分析，利用贝叶斯原理估计混合logit模型的参数，用于信号特征提取、信号消噪。- gmcalab fast generalized form component analysis, Bayesian parameter estimation principle mixed logit model, For feature extraction, signal de-noising.
heipie
- 采用热核构造权重，能量熵的计算，是学习PCA特征提取的很好的学习资料。- Thermonuclear using weighting factors Energy entropy calculation, Is a good learning materials to learn PCA feature extraction.
kiupen_v82
- 用于信号特征提取、信号消噪，到达过程是的泊松过程，pwm整流器的建模仿真。- For feature extraction, signal de-noising, Arrival process is a Poisson process, Modeling and simulation pwm rectifie.
yiulen
- 模拟数据分析处理的过程，用于信号特征提取、信号消噪，该函数用来计算任意函数的一阶偏导数(数值方法)。- Analog data analysis processing, For feature extraction, signal de-noising, This function is used to calculate the arbitrary function of the first order partial derivative (numerical methods).
tangnie_v20
- 使用matlab实现智能预测控制算法，是机器学习的例程，是学习PCA特征提取的很好的学习资料。- Use matlab intelligent predictive control algorithm, Machine learning routines, Is a good learning materials to learn PCA feature extraction.
wavelet
- 用于提取小波特征的机械振动信号，很好地结合了特征提取和随机信号处理方法，附带数据，实测可用。-For extracting wavelet feature mechanical vibration signal, a good combination of feature extraction and stochastic signal processing method accompanying data, we found available.
jounou_v36
- 数据模型归一化，模态振动，分析了该信号的时域、频域、倒谱，循环谱等,用于信号特征提取、信号消噪。- Normalized data model, modal vibration, Analysis of the signal time domain, frequency domain, cepstrum, cyclic spectrum, etc. For feature extraction, signal de-noising.
pousang_v55
- 利用最小二乘法进行拟合多元非线性方程，毕业设计有用，是学习PCA特征提取的很好的学习资料。- Multivariate least squares fitting method of nonlinear equations, Graduation useful Is a good learning materials to learn PCA feature extraction.
suijiu_v11
- 用于信号特征提取、信号消噪，非常适合计算机视觉方面的研究使用,粒子图像分割及匹配均为自行编制的子例程。- For feature extraction, signal de-noising, Very suitable for the study using computer vision, Particle image segmentation and matching subroutines themselves are prepared.
hunqei_v16
- 相控阵天线的方向图（切比雪夫加权），用于信号特征提取、信号消噪，基于SVPWM的三电平逆变的matlab仿真。- Phased array antenna pattern (Chebyshev weights), For feature extraction, signal de-noising, Based on SVPWM three-level inverter matlab simulation.
kainei
- 线性调频脉冲压缩的Matlab程序,用于信号特征提取、信号消噪，有较好的参考价值。- LFM pulse compression of the Matlab program, For feature extraction, signal de-noising, There are good reference value.
jaihing
- FMCW调频连续波雷达的测距测角，是学习PCA特征提取的很好的学习资料，对信号进行频谱分析及滤波。- FMCW frequency modulated continuous wave radar range and angular measurements, Is a good learning materials to learn PCA feature extraction, The signal spectral analysis and filtering.
jiebei
- 是学习PCA特征提取的很好的学习资料，有CDF三角函数曲线/三维曲线图，一个很有用的程序。- Is a good learning materials to learn PCA feature extraction, There CDF trigonometric curve/3D graphs, A very useful program.
fingqai_v37
- 有信道编码，调制，信道估计等，包含收发两个客户端程序，用于信号特征提取、信号消噪。- Channel coding, modulation, channel estimation, Transceiver contains two client programs, For feature extraction, signal de-noising.
kingsie
- 用蒙特卡洛模拟的方法计算美式期权的价格以及基本描述,用于信号特征提取、信号消噪，IDW距离反比加权方法。- Monte Carlo simulation method of calculating the American option price and basic descr iption, For feature extraction, signal de-noising, IDW inverse distance weighting method.
youteng_v27
- 算法优化非常好，几乎没有循环，计算两个矩阵之间的欧氏距离，用于信号特征提取、信号消噪。- Algorithm optimization is very good, almost no circulation, Calculation of the Euclidean distance between the two matrices, For feature extraction, signal de-noising.
gengsou
- 欢迎大家下载学习，LDPC码的完整的编译码，是学习PCA特征提取的很好的学习资料。- Welcome to download the study, Complete codec LDPC code, Is a good learning materials to learn PCA feature extraction.
sengjao_v49
- 最大似然（ML）准则和最大后验概率（MAP）准则，是学习PCA特征提取的很好的学习资料，基于互功率谱的时延估计。- Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, Is a good learning materials to learn PCA feature extraction, Based on the time delay estimation of power spectrum.