boost

说明：  EMD的子程序，刚刚忘记了不好意思，真的不是为了刷程序数量(EMD of the subroutine, just forget I am sorry, do not amount to brush program)

使用matlab进行神经网络建模，再用网络模型解决生活中的实际问题(use matlab to build nero network and solve daily problems)

几种图像分割的源码。基于最大类间最大距离比准则的图像分割，基于最大类间最大距离准则的图像分割，最大熵准则的图像分割(Source of several image segmentation. Based on the maximum distance between the maximum class guidelines image segmentation, image segmentation based on the maximum distance between the maximum class guidelines image segmentation, the maximum entropy criterion)

点目标的高分辨一维距离像的仿真程序，代码中会生成回波数据。(Point target high resolution range profile simulation program, the code will be generated in the echo data.)

说明三个分段相连的信号的傅里叶变换和时频分析信号的傅里叶变换和时频分析(Note the three sub-linked signal Fourier transform and time-frequency analysis of signals in Fourier Transform and Time-Frequency Analysis)

1) 已知x=[1:15], y=[12 34 56 78 99 123 165 198 243 277 353 345 303 288 275], 用多项式进行拟合，给出拟合多项式，(所选拟合多项式阶数不同，拟合结果不同，请你选择适当的阶数进行拟合)，并绘出原始数据点、拟合曲线和95％置信区间（confidence bounds）的图形 . 2) 根据下面数学模型建立仿真模型，并在一个示波器中把它们的波形仿真出来。 (1) are known to x = [1:15], y = [12 34 56 78 99 123 165 198 243 277 353 345 303 288 275], with polynomial fitting, given the polynomial fitting, (the election order polynomial fitting different fitting results to differ, please select the appropriate order of fit), and draw original data points, fitting curve and 95 confidence interval (confidence bounds) graphics .2 ) In accordance with the following mathematical model for the establishment of simulation model, and in an oscilloscope waveform in their simulation out.)

It is Radar System design and signal processing matlab code. It provides varios detalail of standard Signal Radar signal processing algorithm .

Pcb programing in matlab

classification with svm

摘　要:提出一种新的基于Pareto多目标进化免疫算法(PMEIA)。算法在每一代进化群体中选取最优非支配抗体保存到记忆细胞文档中 同时引入Parzen窗估计法计算记忆细胞的熵值,根据熵值对记忆细胞文档进行动更新,使算法向着理想Pareto最优边界搜索。此外,算法基于点在目标空间分况进行克隆选择,有利于得到分布较广的Pareto最优边界,且加快了收敛速度。与已有算法相比, PMEIA在收敛性、多样性,以及解的分布性方面都得到很好的提高。(Abstract:This paperproposed a new pareto-based multi-object evolutionary mi mune algorithm(PMEIA). PMEIA selected optmi alnon-dominated antibodieswhichwere then reserved inmemory cellarchive, and introducedParzenwindow to calculate entropy ofmemory cells. Updated thememory cell archive according to entropy ofmemory cells. This guarantees the conver- gence to the true Pareto fron.t Moreover, the performance of clone selection was dependent on distribution in the objective space, whichwas favorable forgetting awidely spreadPareto frontand mi proving convergence speed. Comparedwith the exis- ted algorithms, the obtained solutions ofPMEIA havemuch betterperformance in the convergence, diversity and distribution.)