-
costas
if you would like bpsk costas
- 2010-11-08 21:16:53下载
- 积分：1
-
隐马尔科夫链的原理HMMall
详细介绍了隐马尔科夫链的原理和matlab代码实现，可以运行其中的demo了解hmm的工作原理(Detailed information on hidden Markov chain theory and the matlab code, you can run the demo to understand the working principle hmm)
- 2020-07-07 23:38:57下载
- 积分：1
-
fujian1
dsp实验中对matlab的基本认识实验，NCEPU的dsp实验一(the basic use of matlab in dsp)
- 2010-05-30 15:20:43下载
- 积分：1
-
Data-visualization
讲述数据可视化的最经典的外文书籍 其中讲述了一些利用matlab 来实现可视化的程序(beautiful data visualization)
- 2013-11-07 21:34:13下载
- 积分：1
-
example_1_4
Synchronized Phasor Measurements
and Their Applications (example 1-4)
- 2014-07-19 16:29:45下载
- 积分：1
-
yichuan
遗传算法的优化，对系统参数进行最优控制，找出满足要求的最优解。(Genetic algorithm optimization, optimal control system parameters to find the optimal solution to meet the requirements.)
- 2013-10-07 20:12:13下载
- 积分：1
-
emd
这是EMD算法，该算法是把一个信号分成几个稳定的有自己特征的信号(This is the EMD algorithm； this algorithm is to divide a signal into several stable have their own characteristics of the signal)
- 2015-03-09 21:19:29下载
- 积分：1
-
shuangyinpinxinhaoanjian
双音频信号处理，模拟电话的各个按键的波形发生情况(Dual audio signal processing, waveform of each key of the occurrence of an analog telephone)
- 2013-12-24 12:22:22下载
- 积分：1
-
DOA
针对相干信号的DOA估计，对常规Music，空间平滑Music，双向空间平滑Music，传统LSESPRIT，TEOPLITZ-ESPRIT算法进行仿真并比较(DOA Estimation of the most classic super-resolution algorithm MUSIC and ESPRIT algorithm simulation of irrelevant signals estimates for the coherent signals DOA, conventional Music, spatial smoothing Music, bidirectional spatial smoothing Music, traditional LSESPRIT, TEOPLITZ-ESPRIT algorithm simulation and compare)
- 2020-12-31 10:18:59下载
- 积分：1
-
KNN
K最邻近密度估计技术是一种分类方法，不是聚类方法。
不是最优方法，实践中比较流行。
通俗但不一定易懂的规则是：
1.计算待分类数据和不同类中每一个数据的距离（欧氏或马氏）。
2.选出最小的前K数据个距离，这里用到选择排序法。
3.对比这前K个距离，找出K个数据中包含最多的是那个类的数据，即为待分类数据所在的类。(K nearest neighbor density estimation is a classification method, not a clustering method.
It is not the best method, but it is popular in practice.
Popular but not necessarily understandable rule is:
1. calculate the distance between the data to be classified and the data in each other (Euclidean or Markov).
2. select the minimum distance from the previous K data, where the choice sorting method is used.
3. compare the previous K distances to find out which K data contains the most data of that class, that is, the class to which the data to be classified is located.)
- 2020-10-23 14:37:22下载
- 积分：1
