lorenzsystembyinline

该文档第一部分总结了matlab中的基本函数其表达式，编程以及范例。第二部分讲了如何插值拟合以及查表。里面有非常详细的解释，并且配有程序范例。(The first part of the document summarizes the basic function of matlab in its expression, programming, and example. The second part of the talk about how to fit and look-up table interpolation. There are very detailed explanation, and with a program example.)

这里有最全面的MATLAB作图方法和技巧，介绍详细且简单易懂，最适用于MATLAB初学者(There are comprehensive plotting methods and techniques of MATLAB, introduced detailed and easy to understand)

用LMS算法预报时间序列，对比真实值和预报值(LMS algorithm with time-series prediction, compared to the true value and forecast value)

这是关于RADRA仿真的.有如下代码placeClutter.m, plotDistLines.m, plotFOV.m, radarSimulation.m, runRadarSim.m, targetsReturn.m, radarSimulation.fig, analyzBuffer.m, buildAntenaGain.m, createTargets.m, displayTargets.m, handleRadarControlls.m, MTIcalcTargetsV.m(RADRA simulation. PlaceClutter.m following code, plotDistLines.m, plotFOV.m. radarSimulation.m, runRadarSim.m. targetsReturn.m, radarSimulation.fig. analyzBuffer.m, buildAntenaGain.m. createTargets.m, displayTargets.m. handleRadarControlls.m, MTIcalcTargetsV.m)

用MATlab实现的神经算法，比较简单，适用于刚学习粒子群算法的人，大家可以交流一下(MATlab neural algorithm, relatively simple, suitable for people new to the study of the particle swarm optimization (pso) algorithm, we can discuss)

卡尔曼滤波的小程序，是理解卡尔曼滤波的一个很好的例子。(Kalman filtering little procedure, Kalman filter is to understand a good example.)

matlab for student and teacher

本程序为《自适应信号处理》（王永德等译，机械工业出版社）书中101下图LMS/Newton算法和LMS算法比较的源程序，仿真效果和书上一致(This program is " adaptive signal processing" (s Acoustic other translation, Mechanical Industry Press) book 101 The following diagram LMS/Newton algorithm and the comparison of the source LMS algorithm, the simulation results and the same book)

余弦变换压缩，用于图像压缩，压缩比高，且压缩后图像质量仍然很好。适用于灰度图象。(Cosine Transform compression for image compression, compression ratio, and compressed image quality is still good. Apply to the gray-scale image.)

function [clusters,c,F]=fisher_classify(A,B,data) fisher判别法程序 输入A、B为已知类别样本的样本-变量矩阵，data为待分类样本 输出C为判别系数向量 (function [clusters, c, F] = fisher_classify (A, B, data) fisher discriminant method procedures input A, B for a sample of known types of samples- variable matrix, data to be classified as output samples for discriminant coefficient vector C)