eigenvalue-problem-of-PDE

这是篇关于matlab环境下的数字滤波器的论文，MATLAB在数字滤波器设计中的应用(This is a chapter on the environment matlab digital filter paper, MATLAB in Digital Filter Design Application)

此文件为ｍｉｍｏ的仿真，一个是师兄的，可以运行(MIMO　SIMULATION)

matlab图形操作指令,可以方便很好掌握图形操作。(matlab graphics operations command)

卡尔曼滤波器完成多传感器数据融合。对多个机器人的不同传感器数据进行融合估计足球精确位置。(Kalman filter to complete the multi-sensor data fusion. Multiple robots of different sensor data fusion estimation football precise location.)

说明：  KL变换滤波，matlab程序，用于地震资料处理(KL transform filter, matlab program for seismic data processing )

将txt中的数据导入matlab中并形成x1,y1,x2,y2...，的变量名，方便管理和调用(Read data in txt file into matlab and generate variables like x1,y1,x2,y2 and so on. Manage the variables for convenience)

MFCC参数和LPCC参数需要频繁使用的语音的说话人识别分析还需要使用大量的这些特征参数,这就需要研究MFCC和LPCC算法和参数提取的原则。因为我们需要了解与人交谈的过程中,我们要进行建模 率不构成线性关系，而更偏向于对数关系，这就使得人耳对低频信号比高频信号更敏感。Mel频率的提出是为了方便人耳对不同频率语音的感知特性的研究。频率与Mel频率的转换公式为(MFCC parameters and LPCC parameters are parameters of speaker recognition which requires frequent use of voice analysis also need to use a lot of these characteristic parameters, This requires research MFCC and LPCC algorithm and the principle of parameter extraction. Because we need to understand the process of talking to people, we want to conduct a speaker modeling the perception of the human ear does not constitute a linear relationship with frequency, and prefer to logarithmic relationship, which makes the human ear is sensitive to low frequency signal is better than high frequency signal. Mel frequency is put forward in order to facilitate the ear of the study of speech perception characteristics of different frequency. For frequency and Mel frequency conversion formula)

You don t need any previous experience in programming to learn C++ with this book. This book starts you the beginning and teaches you both the language and the concepts involved with programming C++.

MATLAB 牛顿下山法的一个小程序，原理就不详细说了。因牛顿迭代法受初值选取的限制，为防止迭代发散，对迭代过程再附加一项要求:|f(x(k+1))|<|f(x(k))|,将牛顿法迭代的结果:x(k+1) =x(k)-f(x(k))/f (x(k))和前一近似值x(k)适当加权平均做为新的改进值:x(k+1)=u*x(k+1) +(1-u)*x(k),其中u(0<> 迭代时u取1开始，逐次减半计算，直至附加要求符合为止。实例计算中x(k)=x(0)不变，只更新u和x(k+1)，直至:|f(x(k+1))|<|f(x(k))|（即|f(x(1))|<|f(x(0))|)，然后更新下山因子为u=1,继续以牛顿法迭代。(MATLAB Newton-down a small program, the principle is not explained in detail. Newton iteration by the initial value of the selected limit, in order to prevent additional iterative divergence, the iterative process a request: | f (x (k+1)) | < | f (x (k)) | Newton iteration Results: x (k+1) ' = X (k)-f (x (k))/f' (x (k)) and a previous approximation x (k), appropriately weighted average as a new improved value: x (k+1 ) = u* x (k+1) ' + (1-u)* x (k), where u (0 < u> iteration take successive calculated at 50 until the additional requirements found so far. examples of calculation of X ( k) = x (0) unchanged profile u and x (k+1, hungry) until: | f (x (k+1)) | < | f (x (k)) | (i.e. | f (x (1)) | < | f (x (0)) |), then profile downhill factor for u = 1, to continue to Newton iteration.)

这是我找到的计算关联维数的ＭＡＴＬＡＢ程序，应该是比较好的(This is what I find the calculation of correlation dimension of the MATLAB program, it should be relatively good)