fangzhen

先定义允许状态S={(x,y)|x=0或3,y=0,1,2,3 x=y=1,2}, 允许决策D={(u,v)|u+v=1,2},求dk<-D(k=0,1,2,…,n),使 状态Sk<-S按照状态转移律S(k+1)=S(k)+(-1)^k*dk,由初始状态 S1=[3,3]经有限步到达状态S(n+1)=[0,0].此为求解多步决策问题( First define to allow the state S = ((x, y) | x = 0 or 3, y = 0,1,2,3 x = y = 1,2), to allow the decision-making D = ((u, v) | u+ v = 1,2), seek dk < -D (k = 0,1,2, ..., n), so that state Sk < -S state transition in accordance with law S (k+1) = S (k )+ (-1) ^ k* dk, from the initial state S1 = [3,3] by the finite-step to reach the status S (n+1) = [0,0]. This is a multi-step decision-making problem solving)

通信系统的自适应均衡器，能够恢复失真的PSK信号(Adaptive equalizer communication system, a PSK signal to recover distorted)

Single Phase Induction Machines

说明：  基于OC算法的连续体动力学机构拓扑优化设计代码，值得下载收藏(The code of topology optimization design of continuum dynamics mechanism based on OC algorithm is worth downloading and collecting.)

基音检测： 对给定语料估计其基音周期。 要求用MATLAB或C语言实现有关基音检测算法，并给出检测结果与实验报告 实验语料：MaoYiSheng.raw 发音：“茅以升”；时长0.9s；采样率：8kHz；量化精度为16bits/sample；数据格式 LSB,MSB 分析帧长:20ms(160样点) 总帧数：45(Pitch Detection: For a given corpus to estimate the pitch period. Require MATLAB or C language on the pitch detection algorithm, and gives experimental results with the experimental report corpus: MaoYiSheng.raw pronunciation: " Mao Yisheng" duration 0.9s sampling rate: 8kHz quantify the accuracy of 16bits/sample data format LSB, MSB of frame size: 20ms (160 samples) the total number of frames: 45)

进一步加深对卡尔曼滤波算法的理解，了解卡尔曼滤波器算法的基本特点，掌握卡尔曼滤波算法应用研究的基本步骤和方法(Kalman filter algorithm to further deepen the understanding and knowledge of the basic characteristics of the Kalman filter algorithm, Kalman filter algorithm is applied to master the basic steps and methods of research)

跟踪滤波方法：概率数据互联。封装性能好！(Tracking filter method: probabilistic data association. Package good performance!)

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.)

改进的人工势场算法。对比传统的算法，对目标不可达问题进行了改进。(Improved artificial potential field algorithm. Compared with the traditional algorithm, the problem of target inaccessibility is improved.)

Implementing quadrature modulation and demodulation of analog signals in digital signal processing, using MATLAB