ejercicio1

针对EMD经验模态分解不能找到instfreq的问题(For empirical mode decomposition EMD can not find instfreq problems)

改进的属性约简算法，能够更快速地实现数据的分类。(Improved attribute reduction algorithm can more quickly achieve classification data.)

是一种双隐层反向传播神经网络，双向PCS控制仿真，结合PCA的尺度不变特征变换(SIFT)算法。( Is a two hidden layer back propagation neural network, Two-way PCS control simulation, Combined with PCA scale invariant feature transform (SIFT) algorithm.)

金融数据的清洗程序，适合于时间序列的整理和初步分析使用(Cleaning process financial data for time series analysis uses the collation and preliminary)

基于Wiener滤波的语音消噪Matlab源程序 调用方式： output=WienerScalart96(signal,fs,IS) signal－－输入含噪语音 fs－－－－语音信号采样率 IS－－－－起始静音端，用来估计噪声方差（秒做单位，默认是0.25秒） (Wiener filter-based de-noising of the voice call Matlab source code the way: output = WienerScalart96 (signal, fs, IS) signal- voice input with noise fs---- voice signal sampling rate IS---- Start mute end used to estimate the noise variance (units of seconds to do, the default is 0.25 seconds))

延迟码的波形图。通过matlab工具仿真出了延迟码的时域和频域波形()

Electrical Load flow using Matlab

子载波 功率分配的问题，用matlab仿真用的，可以直接用matlab打开 (Subcarrier power allocation problem matlab simulation matlab open)

基于羽田孜模型的预知的路径损耗 基于爱德华兹和德金模型的路径损失预知 基于帕森斯与伊布拉因模型的路径损耗预知(Tsutomu Hata model based on predictable path loss payments based on Edwards and the German model to predict the path loss model based on Parsons and Ibraim predict the path loss)

病态线性方程组的计算题，涉及Gauss消元法、雅可比迭代法、高斯-赛德尔迭代法、最速下降法和共轭梯度法。每一个方法，都编写一个m文件，封装成函数的形式。然后通过总的HilbLineEquSet.m文件来调用执行，画出误差曲线图，得到运行结果。总的Matlab程序流程，如下所示： 病态方程组的计算包括：HilbLineEquSet.m、gauss.m、jacobi.m、gauss_seidel.m、fastest_descend.m和conjugated_grad.m六个文件。 程序执行结果包括：求解结果、迭代次数、迭代误差数据、误差曲线图等。 (Morbid Linear Equations calculation problems involving Gauss elimination method, Jacobi' s iterative method, Gauss- Seidel iterative method, steepest descent method and conjugate gradient method. Each method, all the preparation of an m file, packaged in the form of a function. Then the total HilbLineEquSet.m file to invoke the implementation of draw error curve, to be running results. General Matlab program flow, as follows: the calculation of morbid equations include: HilbLineEquSet.m, gauss.m, jacobi.m, gauss_seidel.m, fastest_descend.m and conjugated_grad.m six documents. Procedures for implementation of the findings include: solving a result, the number of iterations, iterative error data, error curve and so on.)