用matlab提取pch文件中的刚度矩阵与质量矩阵

Matlab Coce for PCA 3

研究并分析了B P神经网络的结构和特点，针对不足之处提出改进方法。在改进的基础 上建立神经网络软件可靠性新模型。通过MATL AB仿真工具进行了实例仿真，证实该新模型比传统 模型预测精度高，泛化能力强(Research and analysis of the structure and characteristics of BP neural network, an improved method for the shortcomings. In the modified base The establishment of a new model of neural network software reliability. By MATL AB simulation tool was simulated, confirmed that the new model than the traditional Model predicts high precision, strong generalization ability)

control d un macuhine à courant continu par logique floue

心音信号进行小波消噪，效果较好，可直接使用(Heart sound signal wavelet denoising, better, can be used directly)

用Burg法进行功率谱预测的函数，对信号建立AR模型递推估计(Burg method with projected power spectrum function, the signal is estimated that the establishment of recursive AR model)

基于matlab编程的二分法的程序与分析(Based on the the matlab programming dichotomy of program and analysis)

直接序列扩频（Direct Sequence Spread Spectrum）工作方式，简称直扩方式（DS方式）。就是用高速率的扩频序列在发射端扩展信号的频谱，而在接收端用相同的扩频码序列进行解扩，把展开的扩频信号还原成原来的信号。(irect Sequence Spread Spectrum (Direct Sequence Spread Spectrum), hereinafter referred to as Direct expansion mode (DS). Is a high rate of spread spectrum sequence extension in signal spectrum, and at the receiving end with the same spread spectrum code sequence for expansion, the expansion of the spread spectrum signal back into the original signal. )

The book describes a variety of optimization problems: unconstrained, constrained, gradient, and nongradient techniques duality concepts multiobjective optimization linear, integer, geometric, and dynamic programming with applications and finite element–based optimization

哈弗曼编码,在matlab中实现,源代码(huffman encode)

Description All the filters have needed neighbor data the current pixel in the image,here this function(res_window=cover_window(how many neighbor value for row ,how many neighbor value for column ,input image) produce the result( res_window(row,col,total size)) input inp_img=2d data(row * col) w=cover size w=[3 3] = 3x3 cover pixel generation output o_img=result 3d o_img(w(1),w(2),r*col) if inp_img=[1 2 3 4 5 6 ] w=[3 3] Soloution it will construct new image new_img=[0 0 0 0 0 0 1 2 3 0 0 4 5 6 0 0 0 0 0 0] result o_img(:,:,1)=[0 0 0 0 1 2 0 4 5] o_img(:,:,2)=[0 0 0 1 2 3 4 5 6] finally o_img(1:3,1:3,6) example a=imread( pears.png ) b=rgb2gray(a) c=cover_window( b,[3 3]) 3x3 window construction