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matlab examples of neural network

底部剪力法求地震作用力 matlab 源程序(Base shear method matlab source)

基于自组织神经网络的矢量量化编码Ｍatlab程序(Based on self-organizing neural network vector quantization coding Matlab procedures)

利用Matlab自带的SVD函数，对带地形的二维数据场进行SVD分析(That comes with Matlab SVD function, SVD analysis on the two-dimensional data field with topography)

this is lpcc matlab code

Flash file animation Imitating a James bond presentation with homer simpson with a gun The homer s eye moves too A mask and combination of movements flash file

合成地震记录是用声波测井或垂直地震剖面资料经过人工合成转换成的地震记录（地震道） 合成记录的制作是一个简化的一维正演的过程，合成记录F(t)是地震子波S(t)与反射系数R(t)褶积的结果。(Synthetic seismogram is sonic or vertical seismic profile data after conversion into synthetic seismograms (seismic trace) the production of synthetic seismogram is a simplified one-dimensional forward modeling process, synthetic seismogram F (t) is the seismic wavelet S (t) and the reflection coefficient R (t) the convolution results.)

说明：  多种方式求解TSP问题，有遗传算法，A*算法，D算法，模拟退火，神经网络等(A variety of ways to solve TSP problem, a genetic algorithm, A* algorithm, D algorithm, simulated annealing, neural networks)

GCP是地面控制点的缩写，主要是处理地面控制点的类，包括控制点的读取处理以及写出。(GCP is an acronym for ground control points, mainly handling ground control points, including read and write process control points.)

说明：  （1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象； （2）将所有子图象按列相接变成一个81维的行向量； （3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量； （4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。 （5）求出所有子块的特征向量。 ((1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image (2) all sub-images according to out-phase into a 81-dimensional row vector (3) all 200 lines for KL transform vector, derived its corresponding covariance matrix of eigenvectors and eigenvalues, in descending order by eigenvalue and the corresponding eigenvector (4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the PCA, the original image block to the 40 feature vectors on the projection, the projection coefficients obtained by this sub-block eigenvector. (5) calculated for all sub-block eigenvector.)