资源列表
houfeng
- LZ复杂度反映的是一个时间序列中，包括广义互相关函数GCC时延估计，有较好的参考价值。- LZ complexity is reflected in a time sequence, Including the generalized cross-correlation function GCC time delay estimation, There are good reference value.
giu_cs28
- 信号处理中的旋转不变子空间法，粒子图像分割及匹配均为自行编制的子例程，包含了阵列信号处理的常见算法。- Signal Processing ESPRIT method, Particle image segmentation and matching subroutines themselves are prepared, Contains a common array signal processing algorithm.
MSLS
- MSLSⅠ多步递推最小二乘法 Msls分三步对系统和噪声模型进行辨识，采用脉冲序列作为辅助系统模型，用计算输出数据；用原输出数据计算,用递推最小二乘方法分别对系统参数和模型参数进行估计。 -MSLS Ⅰ recursive least squares multi-step Msls three steps on the system and noise model identification, the use of pulse sequence as a supplementary s
shuzhi_6
- 方法一Gauss消去法 采用一般Gauss消去法解方程组 方法二：列主元Gauss消去法 先找出没一步中没列的主元然后应用Gauss消去法解方程组 -A Gauss elimination method Gauss elimination method using the general method of solving system of equations II: main-element Gauss elimination method did not find som
c++
- 利用c++实现遗传算法的求出程序的优化设计-use genetic algorithm calculated the optimal design procedures
guihua
- 算法设计分析，动态规划，与背包问题类似的最优化求解问题
ga_cpp
- 用遗传算法求解FT、LA问题的源程序，非常具有参考价值！-genetic algorithm FT, the source LA, and is quite useful!
maopaofa
- 该程序实现了冒泡法排序，程序简单易懂，适合初学者-The program achieved a sort of bubble method, straightforward procedure, suitable for beginners
poujen_V5.6
- 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，通过虚拟阵元进行DOA估计。- Using MATLAB dynamic clustering or iterative self-organizing data analysis, Can realize the two-dimensional data clustering, Conducted through virtual array DOA estimation.
chap03
- 计算机常用数值算法与程序(C++版 ) chap03 随机数产生-Computer numerical algorithm and procedures used (C++ Version) chap03 random number generator
cordic_1-0
- 在通訊系統中常見到的cordic，是個用很少複雜度就能實現三角函數的電路，檔案中有C語言的CORDIC程式-In the communication system common to cordic, is a little complex to use trigonometric functions will be able to realize the circuit, the file has C language program CORDIC
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.