rbfMATLAB

选取滤波器（低通滤波器和带阻滤波器）采样点之间信号并与对应的输入信号进行比较的源程序。 (Select filter (low-pass filter and band stop filter) the signal between sampling points and the corresponding input signal for comparison of the source.)

该程序是用L-Z复杂度算法检测VF信号的算法，并用的MIT-BIH数据库中的数据加以实现的。(The program is used LZ complexity algorithm to detect VF signal algorithm, and use the MIT-BIH data in the database to be achieved.)

针对粒子群算法存在局部最优，易陷入局部最小值的问题，自适应权重粒子群优化算法(For particle swarm optimization, there are some problems, which are easy to fall into local minimum, and the adaptive weight particle swarm optimization algorithm is easy to fall into local minimum.)

对采集来的图像进行预处理，去噪、滤波、提取出边缘的图像。(The collected image preprocessing, noise reduction, filtering, extracting the edge image.)

Demo RVM relevance vector machine matlab

说明：  可以教你快速入门的MATLAB教程，个人觉得很好，值得学习一下(not bad)

说明：  本程序适用于一阶超静定问题，是一种三赶简单问题.(This procedure applies to first-order indeterminate problems is a three time for simple questions.)

1、工具箱：LS_SVMlab Classification_LS_SVMlab.m - 多类分类 Regression_LS_SVMlab.m - 函数拟合 2、工具箱：OSU_SVM3.00 Classification_OSU_SVM.m - 多类分类 3、工具箱：stprtoolsvm Classification_stprtool.m - 多类分类 4、工具箱：SVM_SteveGunn Classification_SVM_SteveGunn.m - 二类分类 Regression_SVM_SteveGunn.m - 函数拟合 (a toolbox : LS_SVMlab Classification_LS_SVMlab.m-over classification Regression_LS_SVMlab.m-function fitting 2, toolbox : OSU_SVM3.00 Classification_OSU_SVM.m-over classification 3, toolbox : stprtool svm Classification_stprtool.m-over category Categories 4, toolbox : SVM_SteveGunn Classification_SVM_SteveGun n.m-II classification Regression_SVM_SteveGunn.m-fitting function)

计算信号相关性的传统方法，利用卷积来计算。(The traditional calculation of signal correlation methods, the use of convolution calculation.)

《系统辨识与MATLAB仿真.pdf》 侯媛彬 汪梅 王立琦 本书共分8章，第1、2章为辨识的基本概念、理论基础和古典辨识方法；第3至6章为现代辨识内容，其中第3章是最小二乘参数辨识，第4章是梯度校正参数辨识，第5章是极大似然法的参数辨识方法，第6章是自适应参数辨识；第7、8章为复杂的非线性系统的智能辨识和混沌辨识，其中第7章是非线性系统的神经网络辨识；第8章是Volterra辨识方法、复杂系统的混沌现象及其辨识。从第2至7章，各章均包含开发的相应程序及其程序剖析。 (" System Identification and MATLAB simulation. Pdf" Hou Yuanbin Wang Mei Wang Liqi book is divided into eight chapters, Chapters 1 and 2 for the identification of the basic concepts, theoretical foundation and classical identification methods first 3-6 chapters of modern identification content, Chapter 3 is the least squares parameter identification, Chapter 4 is the gradient correction parameter identification, Chapter 5 is the maximum likelihood parameter identification method, Chapter 6 is an adaptive parameter identification Chapters 7 and 8 for complex Intelligent identification of nonlinear systems and chaos identification, which is in Chapter 7 of the neural network nonlinear system identification Chapter 8 is Volterra identification method, complex systems and chaos identification. From the first 2-7 chapters, each chapter contains procedures for the development of appropriate programs and their analysis.)