MATLAB-Code-of-Radar

应用小区域方差对比，程序简单，一些自适应信号处理的算法，包括脚本文件和函数文件形式，单径或多径瑞利衰落信道仿真，鲁棒性好，性能优越，用于信号特征提取、信号消噪，匹配追踪和正交匹配追踪。( Application of small area variance comparison, simple procedures, Some adaptive signal processing algorithms, Including script files and function files in the form, Single path or multipath Rayleigh fading channel simulation, Robustness, superior performance, For feature extraction, signal de-noising, Matching Pursuit and orthogonal matching pursuit.)

考虑雨衰 阴影 和多径影响，利用自然梯度算法，非归零型差分相位调制信号建模与仿真分析 ，能量熵的计算，借鉴了主成分分析算法（PCA），基于负熵最大的独立分量分析，包括最小二乘法、SVM、神经网络、1_k近邻法。( Consider shadow rain attenuation and multipath effects Use of natural gradient algorithm, NRZ type differential phase modulation signal modeling and simulation analysis, Energy entropy calculation, It draws on principal component analysis algorithm (PCA), Based on negative entropy largest independent component analysis, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.)

自己在硕士期间用到的各种模式识别，机器学习，数据挖掘算法的matlab程序。C4_5,NN，SVM，adaboost,KNN等(During their Master used a variety of pattern recognition, machine learning, data mining algorithm matlab program. C4_5, NN, SVM, adaboost, KNN, etc.)

he m-file helps you o understand the generatio,transmission and reception of an ofdm signal, and also shows the effect of the channel noise and the HPA on the ofdm signal.

This code is for multiuser detection in communication

Program to implement selection sort

换热器设计计算，基于matlab设计源码(The design of heat exchanger can be input parameters to obtain results)

EM code for neural net(matlab code for neural net parameter estimation )

HOG特征的MatlAB实现，在图像识别中应用非常广泛。(HOG features of MatlAB，Is widely used in image recognition。)

说明：  针对一般的粒子群优化(PSO)学习算法中存在的容易陷入局部最优和搜索精度不高的缺点,对改进型PSO算法进行研究。由于惯性权重系数ω对算法是否会陷入局部最优起到关键的作用,因此,通过改变惯性权重ω的选择,对惯性权重系数采取线性减小的方法,引入改进型的PSO算法。采用改进的PSO算法对PID控制器进行参数优化并把得到的最优参数应用于控制系统中进行仿真。仿真实验结果表明:改进型PSO算法不会陷入局部最优,能得到全局最优的PID控制器的参数,并使得控制系统的性能指标达到最优,控制系统具有较好的鲁棒性。(Aiming at the shortcomings of general PSO learning algorithm, which is easy to fall into local optimum and low search precision, the improved PSO algorithm is studied)