erlang

通过使用matlab优化工具箱中的函数fmincon解决有约束的非线性优化问题，有详细的代码过程。(By using the matlab optimization toolbox function fmincon to solve constrained nonlinear optimization problem, has detailed the process code.)

sbs和sfs特征提取的MatLAB源代码用于多维向量提取最优(sbs Feature Extraction)

基于Matlab的双足机器人动力学仿真及仿生控制平台(Dynamic simulation and bionic control platform for Biped Robot Based on Matlab)

Book for Algorithms Collections for DigitalSignalProcessing Applications using Matlab

military handbook for surface to air missile simulation

小波模极大源代码，可用来进行小波降噪或小波模态参数识别，该代码为斯坦福大学源码。(wavelet modulus maxima source code, which can be used for wavelet denoising or wavelet modal parameter identification, The code for the source of Stanford University.)

网上能下载的本书的中文版均缺少89和97两页，又此书已绝版，本人只好从英文完整版中摘录缺少的内容，分别补全了这两页的内容。注：英文完整版也已经作为PDF的附件加了进去。 我也上传到了IASK，不能下载的朋友可以去搜索“(中文版)支持向量机导论.rar”(Internet can download the Chinese version of the book were missing two 89 and 97, and the book is out of print, I had to excerpts missing content from the English full version, respectively, to complement the content of the two pages. Note: The full version of English has also been added as an attachment inside a PDF. I also uploaded to the IASK, can not download friends can go search for "(Chinese version) Introduction to Support Vector Machines. Rar")

％ BackgroundRemoval=[true],false ％ Gain=[tsquare],linear ％ BandPass=[paul],fircls ％ CenterFrequency, auto (determined using pburg) ％ BandWidth=auto (a fraction of the CenterFrequency default=0.25) ％ ContrastStretch=[true],false ％ HilbertAmplitude=[true],false ％ HorizontalStacking=1 (a number of traces) ％( BackgroundRemoval = [true], false Gain = [tsquare], linear BandPass = [paul], fircls CenterFrequency, auto (determined using pburg) BandWidth = auto (a fraction of the CenterFrequency default = 0.25) ContrastStretch = [true], false HilbertAmplitude = [true], false HorizontalStacking = 1 (a number of traces))

matlab CVSD算法,请大家参考(matlab CVSD， hope it can help u guys)

PSO和GA的结合，优化结果明显提升，PSO是粒子群优化算法，GA是遗传算法(The combination of PSO and GA improves the optimization results. PSO is a particle swarm optimization algorithm and GA is a genetic algorithm.)