numericalintegral

基于二阶或更高阶统计方法的MIMO系统频域盲辨识(Based on second-order or more higher-order statistics method of frequency domain blind MIMO system identification)

正交频分复用程序解析，小菜多日收集，虽然未必精辟但说不定有你想要的 (Orthogonal frequency division multiplexing analytical procedures, side dishes, multi-day collection, although not brilliant but maybe you want to have the)

OFDM系统的频率偏移量与error的关系曲线(OFDM systems with frequency offset error of the curve)

Ce programme nécessite le matériel suivant

Finite elements analysis for a beam with three joints

LDA人脸识别 rec 程序段 经测试完全可用 希望对大家有用(LDA face recognition program segment rec tested fully available to all of us hope that useful)

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.)

主要是介绍利用matlab软件编写的一份K均值处理的m文件(Matlab software is to introduce the use of a K-means dealing with the preparation of m-file)

以协调控制系统为对象,基于减法聚类的多模型在线辨识算法.(Multi-model line identification algorithm based on subtractive clustering.)

代码实现了平移不变小波框架的快速分解重构算法。(A fast decomposition and reconstruction algorithm based on the translation invariant is included in the pakage. )