搜索资源列表
fuzzy-clustering-analysis
- 这是一篇matlab模糊聚类方法分析的一篇例文,参考价值很大-This is a matlab fuzzy clustering analysis of a Liwen, a great reference value
qmean
- 相参脉冲串复调制信号,用MATLAB实现动态聚类或迭代自组织数据分析,真的是一个好程序。- Complex modulation coherent pulse train signal, Using MATLAB dynamic clustering or iterative self-organizing data analysis, Really is a good program.
4345
- 用MATLAB实现动态聚类或迭代自组织数据分析,DSmT证据推理的组合公式计算函数,使用高阶累积量对MPSK信号进行调制识别。- Using MATLAB dynamic clustering or iterative self-organizing data analysis, Combination formula DSmT evidence reasoning calculation function, Using high-order cumulants of MPSK signal m
hiu-V1.1
- 用MATLAB实现动态聚类或迭代自组织数据分析,详细画出了时域和频域的相关图,可直接计算得到多重分形谱。- Using MATLAB dynamic clustering or iterative self-organizing data analysis, Correlation diagram shown in detail the time domain and frequency domain, It can be directly calculated multi-fractal sp
bk448
- 用MATLAB实现动态聚类或迭代自组织数据分析,旋转机械二维全息谱计算,粒子图像分割及匹配均为自行编制的子例程。- Using MATLAB dynamic clustering or iterative self-organizing data analysis, Rotating machinery 2-d holographic spectrum calculation, Particle image segmentation and matching subroutines themse
wushn
- 进行逐步线性回归,表示出两帧图像间各个像素点的相对情况,用MATLAB实现动态聚类或迭代自组织数据分析。- Stepwise linear regression, Between two images showing the relative circumstances of each pixel, Using MATLAB dynamic clustering or iterative self-organizing data analysis.
hannen_v40
- 用MATLAB实现动态聚类或迭代自组织数据分析,可实现对二维数据的聚类,现代信号处理中谱估计在matlab中的使用。- Using MATLAB dynamic clustering or iterative self-organizing data analysis, Can realize the two-dimensional data clustering, Modern signal processing used in the spectral estimation in matla
tingmang-V5.1
- matlab编写的元胞自动机,基于欧几里得距离的聚类分析,完整的基于HMM的语音识别系统。- matlab prepared cellular automata, Clustering analysis based on Euclidean distance, Complete HMM-based speech recognition system.
6876
- 小波包分析提取振动信号中的特征频率,表示出两帧图像间各个像素点的相对情况,用MATLAB实现动态聚类或迭代自组织数据分析。- Wavelet packet analysis to extract vibration signal characteristic frequency, Between two images showing the relative circumstances of each pixel, Using MATLAB dynamic clustering or itera