matlab

menu bar 实现windows xp的样式(menu bar to achieve the style of windows xp)

在不加噪声的情况下，利用细菌觅食优化算法（BFA)辨识NARMAX模型(In the case without noise, the use of bacterial foraging algorithm (BFA) Identification NARMAX model)

实现分水岭算法分割图像，使用梯度的分水岭分割来解决图像的分割问题(Watershed algorithm for image segmentation, watershed segmentation using the gradient to solve the image segmentation problem)

用matlab编写的径向基函数神经网络程序，有需要的就下载吧，(Prepared using matlab radial basis function neural network program, there is need to download it,)

系统模型辨识中使用维纳滤波器，并对SDA,LMS和RLS算法作比较(system modeling realization using winner filtering ,steepest descend alogrithm,LMS algorithm,RLS algorithm)

matlab sumission for image processing

说明：  Matlab地震波显示程序 Matlab的矩阵数据类型与地震波数据类似 (Matlab seismic display program Matlab matrix data types and seismic wave data similar)

matlab模拟退火算法的TSP算法，并实现以求解得到最优。(matlab simulated annealing algorithm TSP algorithm and implementation.)

有关心电处理的程序，主要是心电去噪，欢迎大家参考(Are concerned about the power handling procedures, mainly ECG denoising, welcome reference)

克里金法假定采样点之间的距离或方向可以反映可用于说明表面变化的空间相关性。克里金法工 具可将数学函数与指定数量的点或指定半径内的所有点进行拟合以确定每个位置的输出值。克里金法是一个多步过程；它包括数据的探索性统计分析、变异函数建模 和创建表面，还包括研究方差表面。当您了解数据中存在空间相关距离或方向偏差后，便会认为克里金法是最适合的方法。该方法通常用在土壤科学和地质中。(This function completes what I have started with the functions variogram and variogramfit. It is not intended to be a highly optimized code for performing kriging but may have educational value. Note that for using kriging, you should download the latest version of variogramfit. kriging uses ordinary kriging to interpolate a variable z measured at locations with the coordinates x and y at unsampled locations xi, yi. The function requires the variable vstruct that contains all necessary information on the variogram. vstruct is the forth output argument of the function variogramfit. This is a rudimentary, but easy to use function to perform an ordinary kriging interpolation. I call it rudimentary since it always includes ALL observations to estimate values at unsampled locations. This may not be necessary when sample locations are not within the autocorrelation range but would require something like a k nearest neighbor search algorithm or something similar. Thus, the alg)