lectureintroductiontoploting

说明：  用MATLAB实现雷达回波信号的仿真(Using MATLAB realize radar signal simulation)

说明：  电力系统潮流PQ分解法5节点和9节点计算程序。(Power flow calculation program PQ decomposition 5 nodes and node 9.)

说明：  关于集中稀疏表示的图像恢复算法，用于去噪、模糊处理(On the image restoration algorithm of concentrated sparse representation, it is used for denoising and fuzzy processing)

matlab basic can be used any where

OFDMA的整数频偏算法仿真,采用1024个子载波.(a arithmetic for the integer freq offset in OFDMA system.the parameter is 1024)

偏最小二乘回归方法及其应用 很有用的书籍 分析数据很有用(Partial least-squares regression method and its application very useful analysis of data useful books)

MATLAB杆系结构分析：Matlat杆系结构分析源代码,包括： 第一章 静定结构内力和位移计算程序（源程序及例题） 第二章 平面刚架矩阵位移法及静力分析程序（源程序及例题） 第三章 平面刚架静力分析程序的改进（源程序及例题） 第四章 空间刚架静力分析及程序设计（源程序及例题） 第五章 刚架动力特性分析及程序设计（源程序及例题）(MATLAB Frame Structure Analysis: Matlat truss structure of the source code, including: Chapter Static Finite element analysis computer program (source code and examples) Chapter plane frame matrix displacement method, and static analysis of programs (source and Example) Chapter static analysis of plane frame process improvement (source code and examples) Chapter IV space frame static analysis and program design (source code and examples) Chapter Frame Dynamic analysis and program design (the source procedures and examples))

基于修改模型的冲突证据组合方法,林志贵,上 海 交 通 大 学学报(Based on the modified model of the conflict in the evidence combination method,林志贵, Journal of Shanghai Jiaotong University)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

Basic rocket launch simulation in Matlab.