voicebox0406

一种基于瞬时无功功率p-q算法的无功电流检测的simulnk仿真(A reactive power P-Q algorithm based on the instantaneous reactive current detection of SIMULNK simulation)

1) 以身高为例，画出男女生身高的直方图并做对比； 2) 采用最大似然估计方法，求男女生身高以及体重分布的参数； 3) 采用贝叶斯估计方法，求男女生身高以及体重分布的参数（假定方差已知，作业请注明自己选定的一些参数情况）； 4) 采用最小错误率贝叶斯决策，画出类别判定的决策面。并判断某样本的身高体重分别为(160,45)时应该属于男生还是女生？为(178,70)时呢？(1) taking height as an example, draw the histogram of male and female students and compare them; 2) the maximum likelihood estimation method was used to calculate the height and weight distribution parameters of male and female students; 3) using Bayesian estimation method to find the parameters of height and weight distribution of male and female students (assuming the variance is known, please indicate some of the parameters selected by the job); 4) using the minimum error rate Bayes decision, draw the decision surface of category decision. And determine the height and weight of a sample respectively (160,45), should belong to the boys or girls? What about (178,70)?)

自动生成实体类（很好用的代码）非常经典的学习代码，我的珍藏(Automatic generation of entity classes (good with the code) is very classical learning the code, my treasure)

说明：  实现ASK调制的详细过程，对学习很有帮助(ASK modulation to achieve the detailed process of learning is helpful)

说明：  利用Matlab实现子功率谱的求解，并实现信号处理和能量算法(Matlab, the solution to achieve sub-power spectrum, and algorithms for signal processing and energy)

比较全的PID控制MATLAB仿真例子程序，直接可以用，包括M文件和SIMULINK仿真。(Compare PID control procedures MATLAB simulation example, can be used directly. M including documents and SIMULINK.)

标准AM振幅调制方式，ma=1时的标准AM振幅调制(Standard AM amplitude modulation, ma = 1 the standard AM amplitude modulation)

Matlab code for expectation-minimization algorithm

做生成数(使用0.1做因子),之后13年的数据按统计方法标准化后输入,训练,仿真,对比结果。(Do generate a few (using 0.1 to do factor), after 13 years of statistical methods in accordance with the standardization of data input, training, simulation, comparing the results.)

Laplacian Eigenmaps [10] uses spectral techniques to perform dimensionality reduction. This technique relies on the basic assumption that the data lies in a low dimensional manifold in a high dimensional space.[11] This algorithm cannot embed out of sample points, but techniques based on Reproducing kernel Hilbert space regularization exist for adding this capability.[12] Such techniques can be applied to other nonlinear dimensionality reduction algorithms as well.