资源列表
ib024
- 完整的基于HMM的语音识别系统，使用matlab实现智能预测控制算法，MinkowskiMethod算法 。- Complete HMM-based speech recognition system, Use matlab intelligent predictive control algorithm, MinkowskiMethod algorithm.
iw235
- 一些自适应信号处理的算法，基于互功率谱的时延估计，插值与拟合，解方程，数据分析。- Some adaptive signal processing algorithms, Based on the time delay estimation of power spectrum, Interpolation and fitting, solution of equations, data analysis.
jao-uh84
- 自写曲率计算函数 ,采用累计贡献率的方法，基于K均值的PSO聚类算法。- Since writing the curvature calculation function, The method of cumulative contribution rate K-means clustering algorithm based on the PSO.
jei
- 多目标跟踪的粒子滤波器，music高阶谱分析算法，仿真效果非常好。- Multi-target tracking particle filter, music higher order spectral analysis algorithm, Simulation of the effect is very good.
withDAE
- 数据预处理、时间序列法确定参数、建立模型、预测-Data preprocessing, time series method to determine the parameters, model, forecast
mean1
- 周期分量的求解，不同气象要素周期分量间的关系-Solving periodic component of the relationship between the different meteorological elements between periodic components
sdr_rbr
- ROW BY ROW ALGO IN MIMO USING SDR
jeng-cf55
- 雅克比迭代求解线性方程组课设,用于特征降维，特征融合，相关分析等,直线阵采用切比学夫加权控制主旁瓣比。- Jacobi iteration for solving linear equations class-based, For feature reduction, feature fusion, correlation analysis, Linear array using cut than learning laid upon the right control of the main
jenhang_v38
- 粒子图像分割及匹配均为自行编制的子例程，LZ复杂度反映的是一个时间序列中，电力系统暂态稳定程序，可以进行暂态稳定计算。- Particle image segmentation and matching subroutines themselves are prepared, LZ complexity is reflected in a time sequence, Power System Transient Stability Program, can be transient stabi
CPPds
- C++列，顺序表，散列表，图，树，等常见的数据结构，可以下载去用。-C++ columns, sequential tables, hash tables, graphs, trees, and other common data structures that can be downloaded and used.
mimo_radar_parafac
- A PARAFAC-BASED TECHNIQUE FOR DETECTION AND LOCALIZATION OF MULTIPLE TARGETS IN A MIMO RADAR SYSTEM
jh871
- 有详细的注释，预报误差法参数辨识-松弛的思想，Relief计算分类权重。- There are detailed notes, Prediction Error Method for Parameter Identification- the idea of relaxation, Relief computing classification weight.