lisandiedai

A Comprehensive Sparse Learning Package(Descriptions: Six sparse algorithms are implemented in this package. They are 1) SVM 2) L1-norm SVM a) Linear Programming SVM b) SC-SVM 3) L0-norm SVM 4) RVM (Relevance Vector Machine) 5) SPC (Sparse Probit Classifier))

simulink搭建的单相光伏并网系统 基于SPWM控制方式(Simulink of single-phase photovoltaic (pv) grid system based on SPWM control mode)

上传了关于解模糊的基本程序，如中国余数定理，余差法等等(From vague on the solutions of the basic procedures, such as the Chinese remainder theorem, and so on more than bad law)

Nonlinear Problem Files Collection (A collection of example programs and algorithms dealing with the solution of nonlinear problems. The following files are included (among others): damped Newton s method (see screenshot) Newton s method example, system case Fixpoint iteration. )

2x2 Alamouti BPSK Rayleigh

this is implementation of fuzzy robot using java and FCL file fuzzy inference system

基于stretch处理的雷达抗距离-速度同步欺骗干扰的仿真(Based on stretch processing of radar distance- speed deception jamming in the simulation)

说明：  改进欧拉法的matlab程序，并且其中有一个例子(Improved Euler method matlab program, and an example)

数字图像相关方法，能够处理图像的变形情况，进而得到图像的应变和切应变(Digital image correlation method, it is possible to deal with the deformation of the image, and then get the strain and shearing strain of the image)

支持向量机是用于分类与回归的技术。由于其自身的诸多缺点，如无法获得概率输出，需要估计一个误 差参数C，以及必须使用Mercer 核函数等。相关向量机算法，克服了SVM 上述缺点，RVM 能获得与SVM 相比拟的推 广性能，并且更为稀疏。在此基础上，文中介绍了一种RVM 回归用于分类的新分类方法，用RVRC 来表示。并通过 实验证明了它的可行性。(Support vector machines for classification and regression techniques. Due to its many shortcomings, such as the probability that the output can not be obtained, need to estimate an error parameter C, and the need to use Mercer kernel function and so on. Relevance vector machine algorithm to overcome these shortcomings of SVM, RVM and SVM can be obtained comparable to promote performance and more sparse. On this basis, the paper introduces a RVM regression was used to classify new classification method, RVRC to represent. And through experiments proved its feasibility.)