Unit1

Matlab中没有现成的Hermite插值函数，必须编写一个M文件实现插值。 设n个节点的数据以数组x0 （已知点的横坐标）， y0（函数值）， y1（导数值）输入（注意Matlat的数组下标从1开始）， m个插值点以数组x 输入，输出数组y 为m 个插值。编写一个名为hermite.m的M文件。 (Matlab is not off-the-shelf Hermite interpolation function, we must realize the preparation of a document M interpolation. N nodes for the data to an array x0 (known point of abscissa), y0 (function value), y1 (numerical derivative) input (note the subscript Matlat array starting at 1), m to the array of interpolation points x input and output array y for m months interpolation. Hermite.m the preparation of a M-file.)

E.S. Gopi.Algorithm collections for digital signal processing applications using matlab. 对算法实现进行了详细介绍。(ES Gopi.Algorithm collections for digital signal processing applications using matlab. On the algorithm described in detail.)

Matlab C++数学库对字符型阵列的操作(======================================================================== CONSOLE APPLICATION : charArrayCreate ======================================================================== AppWizard has created this charArrayCreate application for you. This file contains a summary of what you will find in each of the files that make up your charArrayCreate application. charArrayCreate.dsp This file (the project file) contains information at the project level and is used to build a single project or subproject. Other users can share the project (.dsp) file, but they should export the makefiles locally. charArrayCreate.cpp This is the main application source file. ///////////////////////////////////////////////////////////////////////////// Other standard files: StdAfx.h, StdAfx.cpp These files are used to build a precompiled header (PCH) file named charArrayCreate.pch and a precompiled types file named StdAfx.obj. ////////)

很全面的matlab优化算法的代码集合，包括蚁群免疫等算法都在，很细致。(Very comprehensive collection of matlab code optimization algorithm, including the immune ant colony algorithms such are very detailed.)

《MATLAB神经网络30个案例分析》中的第13个例子，案例13 SVM神经网络中的参数优化---提升分类器性能。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of example, 13 cases of 13 SVM parameters optimization of neural network classifier performance- ascension. Hope to have certain help to everybody! )

example code for Digital Signal Processing Using Matlab

Finland Matlab cannot do regular meshes for non-regtancular regions. However, one can manually do very coarse regular meshes for these type domains. The difficulty is with the edge data e. If this data does not correspond to the orientations given by the PDEtool geometry files, refinement will not work. The function drawedges() will display the order of the edges and their segment numbers. However, this function does not tell how the edges are oriented. However, just run the visualization, and if the refinement does not work correctly, you know the problem is with the orientatino of the edge. Remember to also correct the last two rows of the matrix e when changing the orientation. (See the documentation for initmesh for more info.)

最优化计算分析方面的程序入门资料小，适合初学者(Optimization calculation program started small data analysis, suitable for beginners)

在一个移动通信系统中，发射信号是一个未调制的音频x(t)=cos(2πft)，其中f=f0+fi。设标称载波为f0=1GHz，并设fi =512Hz。假定移动用户在一个繁忙的高速公路上距离基站l公里处的地方，从开始从每小时60公里的速度开始，其速度（在0.01秒的时间间隔内测量）以1/2的概率随机的增加或减少1/10公里每小时。试仿真移动用户在这2s的时间间隔内所接收到的信号(及其频谱)。接收信号可以表示为： y(t)=a(t)Cos[2π(fo+f1)(t-△t)] 式中 △(t)=d(t)/c 且 a(t)=k/d2(t) 在上面的表达式中d(t)是在t时刻基站与移动用户的距离，c是光速，△(t)是传播延迟，而a(t)是信号在t时刻的衰减，并假设在这2s内a(t)是一个常数。利用复低通等效模型来进行仿真。(In a mobile communication system, the transmitted signal is an unmodulated audio x (t) = cos (2πft), where f = f0+ fi. Let nominal carrier of f0 = 1GHz, and set fi = 512Hz. Assume that mobile users on a busy motorway station l kilometers place, starting the beginning of 60 kilometers per hour, the speed (measured in 0.01 second interval) to increase the probability of a random 1/2 or reducing 1/10 km/h. Simulation test signal which the mobile subscriber in the 2s time intervals of the received (and its spectrum). The received signal can be expressed as: y (t) = a (t) Cos [2π (fo+ f1) (t- △ t)] Where △ (t) = d (t)/c And a (t) = k/d2 (t) In the above expression, d (t) at time t is a base station and a mobile user s distance, c is the speed of light, △ (t) is the propagation delay, and a (t) is the attenuation of the signal at time t, and assuming that 2s within a (t) is a constant. Using complex low-pass equivalent model for emulation.)

state flow program in Matlab simulink.