多种群遗传算法的函数优化算法matlab代码

离散傅里叶变换时实现信号频域分析的第一步，用matlab实现对离散傅里叶变换的描述。(Discrete Fourier transform when the signal frequency domain analysis to achieve the first step, using matlab to achieve a description of discrete Fourier transform.)

说明：  仿真LMS均衡器 采用三径叠加信道，PSK调制，并计算误码性能(LMS equalizer simulation using three-path channel superimposed, PSK modulation, and calculate the BER)

详解MATLAB 数字信号处理[张德丰][程序源代码] 书<<MATLAB 数字信号处理[张德丰]>>全部采用了MATLAB语言编程实现，并结合实例对算法程序进行验证和分析。其中详细讲解了信号的分析基础、系统模型及数据采集分析、信号的变换、模拟滤波器、IIR滤波器设计、FIR滤波器、MATLAB的其他滤波器、随机信号及参数建模、信号在小波分析与处理中的应用等，绍MATLAB在数字信号中的具体应用。 数字信号处理的工程设计人员参考。 (MATLAB source code, from the book"Digital signal processing-using MATLAB" by the Zhang Defeng.)

这是之前上传的Extract_test_IMFCC程序里面调用的子函数，作配合使用，请看清楚再下载.(This is one subfunction of Extract_test_IMFCC uploaded before.Since pudn.com cannot upload several functions, they must divided.)

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.)

基于连续hopfield网络的TSP问题解决方法(Based on continuous hopfield networks TSP problem solving)

transform a file to dct domain

slam代码，扩展卡尔曼滤波，输入时两个轮子的速度以及采集到的Landmark标志点与移动机器人的相对坐标(slam code, extended Kalman filter, the input speed of the two wheels, as well as the acquisition Landmark signs point to the relative coordinates of the mobile robot)

Program for interpolation facial image

Matlab ART algorithm for finding length of beam through each pixel