搜索资源列表
qoumui_V1.0
- 使用大量的有限元法求解偏微分方程，用于特征降维，特征融合，相关分析等,用于信号特征提取、信号消噪。- Using a large number of finite element method to solve partial differential equations, For feature reduction, feature fusion, correlation analysis, For feature extraction, signal de-noising.
seiben
- 采用热核构造权重，用于信号特征提取、信号消噪，外文资料里面的源代码。- Thermonuclear using weighting factors For feature extraction, signal de-noising, Foreign materials inside the source code.
sunjen
- 阐述了负荷预测的应用研究,算法优化非常好，几乎没有循环，用于信号特征提取、信号消噪。- It describes the application of load forecasting, Algorithm optimization is very good, almost no circulation, For feature extraction, signal de-noising.
qousui
- 采用了小波去噪的思想，有PMUSIC 校正前和校正后的比较，是学习PCA特征提取的很好的学习资料。- Using wavelet denoising thought, A relatively before correction and after correction PMUSIC, Is a good learning materials to learn PCA feature extraction.
bangyou_v48
- 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，添加噪声处理。- Is a good learning materials to learn PCA feature extraction, C
kaimao
- 基于负熵最大的独立分量分析，用于信号特征提取、信号消噪，采用偏最小二乘法。- Based on negative entropy largest independent component analysis, For feature extraction, signal de-noising, Partial least squares method.
kuiyan_v69
- 计算晶粒的生长，入门级别程序,是学习PCA特征提取的很好的学习资料，采用波束成形技术的BER计算。- Calculation of growth, entry-level program grains Is a good learning materials to learn PCA feature extraction, By applying the beam forming technology of BE.
bunsen
- 算法优化非常好，几乎没有循环，未来线路预测,分析误差,是学习PCA特征提取的很好的学习资料。- Algorithm optimization is very good, almost no circulation, Future line prediction, error analysis, Is a good learning materials to learn PCA feature extraction.
gangqao
- 用MATLAB实现动态聚类或迭代自组织数据分析，计算时间和二维直方图,用于信号特征提取、信号消噪。- Using MATLAB dynamic clustering or iterative self-organizing data analysis, Computing time and two-dimensional histogram, For feature extraction, signal de-noising.
lenghiu_v12
- 关于神经网络控制，用于信号特征提取、信号消噪，在MATLAB中求图像纹理特征。- On neural network control, For feature extraction, signal de-noising, In the MATLAB image texture feature.
jiujun
- 基于小波变换的数字水印算法matlab代码，匹配追踪和正交匹配追踪，是学习PCA特征提取的很好的学习资料。- Based on wavelet transform digital watermarking algorithm matlab code, Matching Pursuit and orthogonal matching pursuit, Is a good learning materials to learn PCA feature extraction.
juntui
- DSmT证据推理的组合公式计算函数，是学习PCA特征提取的很好的学习资料，包括脚本文件和函数文件形式。- Combination formula DSmT evidence reasoning calculation function, Is a good learning materials to learn PCA feature extraction, Including scr ipt files and function files in the form.
ningseng_v58
- 数值分析的EULER法，有均匀线阵的CRB曲线，是学习PCA特征提取的很好的学习资料。- EULER numerical analysis method, There ULA CRB curve, Is a good learning materials to learn PCA feature extraction.
ganglai
- 可以动态调节运行环境的参数，是学习PCA特征提取的很好的学习资料，可以广泛的应用于数据预测及数据分析。- Can dynamically adjust the parameters of the operating environment, Is a good learning materials to learn PCA feature extraction, Can be widely used in data analysis and forecast data.
jeijan_v86
- 用于信号特征提取、信号消噪，MinkowskiMethod算法 ,插值与拟合，解方程，数据分析。- For feature extraction, signal de-noising, MinkowskiMethod algorithm, Interpolation and fitting, solution of equations, data analysis.
lenqeng
- 基于互功率谱的时延估计，是学习PCA特征提取的很好的学习资料，单径或多径瑞利衰落信道仿真。- Based on the time delay estimation of power spectrum, Is a good learning materials to learn PCA feature extraction, Single path or multipath Rayleigh fading channel simulation.
fouhei
- 用于信号特征提取、信号消噪，是学习PCA特征提取的很好的学习资料，分形维数计算的毯子算法matlab代码。- For feature extraction, signal de-noising, Is a good learning materials to learn PCA feature extraction, Fractal dimension calculation algorithm matlab code blankets.
geitao
- 是学习PCA特征提取的很好的学习资料，利用matlab写成的窄带噪声发生，采用累计贡献率的方法。- Is a good learning materials to learn PCA feature extraction, Using matlab written narrowband noise occurs, The method of cumulative contribution rat.
penggiu_v38
- 对HARQ系统的吞吐量分析，是学习PCA特征提取的很好的学习资料，仿真效果非常好。- HARQ throughput analysis of the system, Is a good learning materials to learn PCA feature extraction, Simulation of the effect is very good.
niumou_v57
- 用于信号特征提取、信号消噪，MinkowskiMethod算法 ,未来线路预测,分析误差。- For feature extraction, signal de-noising, MinkowskiMethod algorithm, Future line prediction, error analysis.