GMVQ

matlab统计分析，好多必须懂的基本知识。有数据实验，分析详细，好教材(matlab statistical analysis, a lot will have to know the basics. Experimental data is analyzed in detail, good materials)

模式识别作业-完全自编仿真程序。先用PCA对IRIS数据集进行降维，然后用最小错误法对降维的数据进行分类。压缩包中既包括matlab源代码，又有自己写的报告，还有.MAT格式的IRIS数据集用作程序调用。程序有详细注释，很容易懂。最后结果输出到txt文件中。(Pattern recognition operations- completely self simulation program. First on the IRIS data set with PCA dimension reduction, and then with the minimum error method to classify the data dimension reduction. Both compressed package matlab source code, but also write their own reports, as well. MAT format, the program calls for IRIS data set. Procedures detailed notes, it is easy to understand. Finally, the output to a txt file. )

采用图像检测算子，包括canny,LOG,prewit,roberts,kirsch算子等，采用MATLAB 开发环境实现图像边缘检测。(complete the edge detection of pictures by all kinds of methods with matlab)

3GPP组织给出的LTE系统高层无线资源控制RRC层规范，希望帮到LTERRC层爱好者。(3GPP LTE system tissue senior radio resource control (RRC) layer specification given, I hope to help LTERRC layer enthusiasts.)

盲信号分离是当前信号处理研究的热点课题之一，在无线数据通信、医学、语音以及地震信号处理等领域有着广阔的应用前景。基于负熵最大的FastICA算法用于实现盲信号分离。该方法的基本思路是以非高斯信号为研究对象，在独立性假设的前提下，对多路观测信号进行盲源分离。在满足一定的条件下，能够从多路观测信号中，较好地分离出隐含的独立源信号。(Blind signal separation is the study of signal processing, one of the hot topics in wireless data communications, medical, speech and seismic signal processing and other fields have broad application prospects. Negentropy based on the largest FastICA Algorithm for Blind Signal Separation realize. The basic idea is based on non-Gaussian signal as the research object, in the independence assumptions on the multi-channel observation signal blind source separation. In meeting certain conditions, be able to multi-channel observation signal, the better to isolate the independent source signal implied.)

irst, let s create a simple vector with 9 elements called a.

QuantRefine using matlab

常微分方程组的四阶RungeKutta龙格库塔法matlab实现(Fourth-order Runge-Kutta method matlab RungeKutta of ordinary differential equations to achieve)

变步长自适应滤波算法，对VS—LMS算法进行了改进，建立了步长因子μ与误差信号e（n）之间另一种新的非线性函数关系．理论分析和计算机仿真结果表明，该关系不仅具有原有算法收敛速度快的优点，而且在低信噪比环境下比原有算法具有更好的抗噪声性能．(The variable step size adaptive filtering algorithm is discussed, and the VS-LMS algorithm is improved. A new nonlinear function relation between the step size factor μ and the error signal e (n) is established. Theoretical analysis and computer simulation results show that the proposed algorithm not only has the advantages of high convergence speed of the original algorithm, but also has better anti- noise performance in the low SNR environment than the original algorithm.)

数学建模竞赛历年获奖论文，希望对大家有帮助！(Mathematical modeling contest winning papers over the years, want to help!)