神经网络用于性能预测

matlab 文件，用于学习m文件，解释m文件的编写及用法(matlab documentation, for learning m paper explaining the preparation of the document m and usage)

说明：  小波改进阈值去噪，并分析去噪后信号的信噪比和均方误差(Improving threshold denoising by wavelet transform and analyzing signal-to-noise ratio and mean square error of denoised signal)

Comparison of the Iterative Stationary of 1st and 2nd order and CG methods on a Tikhonov regularization inverse problem: (Comparison of the Iterative Stationary of 1st and 2nd order and CG methods on a Tikhonov regularization inverse problem:)

code about fdtd gooood2

说明：  上传一份MSA的MATLAB代码示例，希望能有帮助(Upload an example of MSA's MATLAB code, hoping to help)

DFT transformation matrix, eigen values, orthogonality check, graphical representation, testing on a random signal

高斯噪声环境下宽带矢量信号仿真(Gaussian noise environment simulation of broadband vector signal)

Free Convection Past a Vertical Flat Plate

选取windows系统自带的ding.wav信号作为分析对象，在Matlab软件平台下，利用函数wavread对音频信号进行采样，记住采样频率和采样点数，听一下原始声音sound(y, fs, bits)。 （2）音频信号的频谱分析，先画出音频信号的时域波形；然后对音频号进行快速傅里叶变换fft(y,N)，N取32768，画出信号的频谱特性，加深对频谱特性的理解。 （3）根据频谱，反演时域特性，画出时域波形。寻找幅值最大的两个频率，此频率除以fft点数在乘以采样频率就是信号的主频，即可合成信号的时域图形，听一下声音。 （4）对原音频信号进行1024点的分段付立业分析meshgrid （5）根据主要频线合成音频，并画出时域图形，试听合成效果。 （6）采用线性插值（linspace）和傅立业反变换(fliplr, ifft)分别合成音频，并画出时域图形，试听效果。 (err)

复杂网络的一篇综述，相信对学习这方面的人会有所帮助的(In this paper ，first，we use polar coordinates transformation for high-dimensional nonlinear Schrö dinger equation , then use separation of variables and homogeneous balance method ,we get spiral wave solution of this Schrö dinger equation and analyze the desired exact solution through the image.)