cartpole-master

王济老师《MATLAB在振动信号处理中的应用》书中源程序(Wang Ji teacher " MATLAB in Vibration Signal Processing Applications" book source)

Simulink toolbox provides the user with two ways to create S-functions in C: with the automatic builder S-Function Builder and manually, using a template (similar to the creation of S-functions written in MATLAB).

在测量中对水准网进行间接平差的一个小程序，基于matlab编写。(In the measurement of leveling network indirect adjustment of a small program, based on matlab)

windowed fourier transform for fringe analysis

FH通信系统仿真的作业 拿来跟大家分享一下(FH system)

对数字散斑的形成仿真处理有全面的介绍 对研究数字散斑在各种方面的测量很有帮助 很有启发性(Simulation of digital speckle formation process with a comprehensive introduction)

projectreportsimulink bidirectionalconverter

基于二阶泛模型的无模型自适应控制及参数整定，充分利用无模型自适应控制边建模边控制的特点，推导基于二阶泛模型的改进无模型自适应控制，应用辨识出的近似模型针对不同目标函数进行优化(Pan model based on second order approximation model of model-free adaptive control and parameter tuning, make full use of model-free adaptive control side edge control modeling features derived model-free adaptive control model based on improved second-order pan, identify the application The objective function to be optimized for different)

介质层间反射系数及其透射系数的求解，此处是二分之一匹配层结构(Dielectric layer between the reflection coefficient and transmission coefficient of the solution)

实现了 SVM 在医学图像分类中的应用，通过医学图像的实验结果验证了该方法的有效性。 对 SVM 算法进行了深入研究，结合量子行为的粒子群算法的优点，提出了一种量子行为的粒子群算法和 SVM 结合的混合分类模型，应用于医学图像的分类中。 深入研究了 K-means 算法，结合了无监督聚类和监督分类方法的优点，提出了一种 K-means 和 SVM 结合的混合分类模型，并应用于医学图像的分类中。 利用粒子群和量子行为的粒子群算法对 K-means 算法进行了改进，并将改进的K-means 算法与 SVM 算法结合形成全自动分类模型，并且把它应用于医学图像的分类中。 (SVM in the classification of medical image applications, medical images through the experimental results verify the effectiveness of the method. SVM algorithm conducted in-depth research, combined with the quantum behavior of the advantages of particle swarm optimization, PSO and SVM combined behavior of a quantum mixed classification model, applied to medical image classification. In-depth study of the K-means algorithm combines the advantages of unsupervised clustering and supervised classification method, propose a hybrid combination of a K-means and SVM classification model, and applied to the classification of medical images. Improved particle swarm algorithm using particle swarm and quantum behavior of the K-means algorithm, and improved K-means algorithm and SVM algorithm combined with the formation of a fully automatic classification model, and apply it to medical image classification.)