reconstr

matlab code for seismogram generation

利用logistic混沌搜索方法对传统粒子群进行该进，并用于多目标决策(The use of logistic chaotic search method for carrying out the traditional particle swarm into, and used for multi-objective decision making)

这是我本人的程序。主要做的是使用我们的MATLAB录制音乐并加以分析使用的。(This is my procedure. Mainly do is to use our MATLAB music recording and analysis used.)

Generate a random sample of size n a finite mixture estimate of the Old Faithful Geyser data

classification with svm

以“一主三从”主从多机通信系统为物理模型，研究应用马尔可夫链建立仿真算法及蒙特卡洛法建立了数学模型，通过将完整的系统元件化，并对每个元件创立各自的状态转移机模型，仿真运行状态，实现了对于这一通信系统的可靠性建模评估。(" One the main three from the" master-slave multi-communication system for the physical model to study the application of Markov chain Monte Carlo simulation algorithm and the mathematical model established through the will of a complete system components, and the creation of each component their respective state transition model, the simulation run, and the realization of a communication system for the assessment of the reliability of modeling.)

Use of FFT to compute Continuous-Time Fourier Transform

Matlab作一维、二维和三维箭头的源代码。(Matlab for the one-dimensional, two-dimensional and three-dimensional arrow s source code.)

Emergency Diesel Generator Simulink

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)