introduction-for-matlab

遗传算法在matlab中的实例应用 ，可以供初学者学习(Genetic algorithm in matlab examples of the application, you can learn for beginners)

Harada book simulation chapter 3

说明：  这是一本MATLB的中文帮助书，其中有一些本人在使用MATLB的心得。希望对初学MATLB的人有一定帮助(This is a Chinese MATLB help books, some of which I experience in the use of MATLB. MATLB beginner would like to have some people to help)

We present a study of a matrix pencil method for esti- mating parameters (frequencies and damping factors) of exponentially damped and/or undamped sinusoids in noise. Comparison of this method to a polynomial method (SVD-Prony method) shows that the matrix pencil method and the polynomial method are two special cases of a matrix prediction approach but the pencil method is more efficient in computation and less restrictive about signal poles. It is found through perturbation analysis and simulation that, for signals with un- known damping factors, the pencil method is less sensitive to noise than the polynomial method. In Appendix A, a new expression of the Cra- mer-Ran bound for the exponential signals is presented.

基于DWT的音频水印算法,采用小波变换，阿诺德置换实现水印的嵌入和提取。(Audio watermarking algorithm based on Wavelet Transform)

均匀面阵和均匀线阵仿真案例，对于学习阵列信号处理有很大帮助(Uniform linear array and uniform linear array simulation case, for learning array signal processing is very helpful)

VC++调用matlab画图，效果很好！值得看(VC++ Call matlab drawing, the effect is very good!)

可对OFDM信道进行预测，对初学者会有帮助~(OFDM Channel estimators.It will be helpful1)

test, this is only to learn ow to start.

MATLAB函数参考手册，查看matlab函数作用以及功能。( SVMLSPex02.m Two Dimension SVM Problem, Two Class and Separable Situation Difference with SVMLSPex01.m: Take the Largrange Function (16)as object function insteads ||W||, so it need more time than SVMLSex01.m Method from Christopher J. C. Burges: "A Tutorial on Support Vector Machines for Pattern Recognition", page 9 Objective: min "f(A)=-sum(ai)+sum[sum(ai*yi*xi*aj*yj*xj)]/2" ,function (16) Subject to: sum{ai*yi}=0 ,function (15) and ai>=0 for any i, the particular set of constraints C2 (page 9, line14). The optimizing variables is "Lagrange Multipliers": A=[a1,a2,...,am],m is the number of total samples.)