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A function to evaluate a neural nets performance as called from the PSO function

几种图像处理源码 程序代码说明 P0301：数字图像矩阵数据的显示及其傅立叶变换 P0302：二维离散余弦变换的图像压缩 P0303：采用灰度变换的方法增强图像的对比度 P0304：直方图均匀化 P0305：模拟图像受高斯白噪声和椒盐噪声的影响 P0306：采用二维中值滤波函数medfilt2对受椒盐噪声干扰的图像滤波 P0307：采用MATLAB中的函数filter2对受噪声干扰的图像进行均值滤波 P0308：图像的自适应魏纳滤波 P0309：运用5种不同的梯度增强法进行图像锐化 P0310：图像的高通滤波和掩模处理 P0311：利用巴特沃斯（Butterworth）低通滤波器对受噪声干扰的图像进行平滑处理 P0312：利用巴特沃斯（Butterworth）高通滤波器对图像进行锐化处理 (err)

EEMD是针对EMD方法的不足,提出了一种噪声辅助数据分析方法。EEMD分解原理为:当附加的白噪声均匀分布在整个时频空间时,该时频空间就由滤波器组分割成的不同尺度成分组成。(EEMD)

skeleton prunning, extract skeleton from binary images by junction points and curve points and end points.

Gauss Seidel Loadflow matlab example

在matlab，传统的边缘检测直接调用edge函数，但是得到的边缘信息较稀薄，可以通过将sobel,roberts,prewitt三种算子的边缘检测叠加起来，并且对于连续的边缘部分拓宽，使得边缘更连贯清晰(In matlab, traditional edge detection edge direct call function, but thinner than the edge information can be obtained by sobel, roberts, prewitt three edge detection operator stack up, and for widening the continuous edge portion, so that the edge is more consistent clear)

对连续的单一频率离散序列 ，即k=8。 按采样频率 采样，截取长度N分别选N =20和N =16，观察其DFT结果的幅度谱。 (Discrete to continuous sequence of single frequency, ie k = 8. According to the sampling frequency sampling, truncation length N = 20 N, respectively, and selected N = 16, observed the results of its DFT magnitude spectrum.)

一个简单实用的预测控制matlab程序，对初学者有很大的帮助(A simple and practical predictive control matlab program, a great help for beginners)

matlab与C语言的接口教程，能将matlab程序语言 直接转换为c语言，非常详细，千万不过错过，快点下吧！！！！(the interface of matlab with C language )

GMDH_main为GMDH主函数； variable_Combin为输入层初始变量选为x1,x1^2,x1*x2,x2^2,x2时的输入变量矩阵值 variable_select计算X_train训练输入数据,Y_train训练输出数据,X_test测试输入数据,Y_test测试输出数据 Combin为求变量的两两组合 Sym_Combin为求符号变量的两两组合 PE_AIC求每层各神经元的参数估计W，及训练数据在参数估计后估计的输出out_train，测试数据在参数估计后的估计输出out_test，还有与实际比较的误差平方和PESS， 以及准则值AIC sym_representation求最终的输入输出符号表达式 Criterion_value求准则值 (GMDH_main main function for the GMDH variable_Combin initial variables chosen for the input layer, x1, x1 ^ 2, x1* x2, x2 ^ 2, x2 the value of the input variable matrix calculation X_train variable_select training input data, Y_train training output data, X_test test input data, Y_test for the sake of the test output data Combin pairwise combination of variables for the sake of symbolic variables Sym_Combin pairwise combinations PE_AIC neurons find each parameter estimation of the W, and after the training data is estimated in the estimation of output out_train, test data After the parameter estimates of the output estimated out_test, also compared with the actual sum of squared errors PESS, as well as the final criterion value AIC sym_representation seek the input and output values ​ ​ of symbolic expressions Criterion_value find criteria)