dagc_final

说明：  低通滤波器 基于MATLAB的低通滤波器，Z变换实现(low-pass filter based on MATLAB low-pass filter, Z transform)

灰色预测gm(1,1),gm(1,n)能有效快速解决灰色预测的处理问题。(gm(1,1),gm(1,n) can sole the problem of GM model easily)

Detector de tono DTMf

对于初学者具有参考意义，是一种双隐层反向传播神经网络，最小均方误差（MMSE）的算法，通过虚拟阵元进行DOA估计，迭代自组织数据分析，FIR 底通和带通滤波器和IIR 底通和带通滤波器。( For beginners with a reference value, Is a two hidden layer back propagation neural network, Minimum mean square error (MMSE) algorithm, Conducted through virtual array DOA estimation, Iterative self-organizing data analysis, Bottom-pass and band-pass FIR and IIR filter bottom pass and band-pass filter.)

说明：  gm11的matlab程序，天然气消耗量的例子进行建立gm11模型(Matlab program of gm11,Establishment of gm11 model with examples of natural gas consumption)

plot of 5 variable in a graph

说明：  带约束多变量DMC控制，能处理带约束的多变量DMC的控制问题(Constrained multivariable DMC control, able to handle constrained multivariable control problem of DMC)

matlab 实现数据的短时傅里叶变换 自己测试通过(short time Fourier transform matlab achieve data test by)

本书主要介绍如何运用Matlah与C.C + +进行混合程序设计。共分8章。主要包括Matlab程序设计初步、Matlab编译器、Matlab与C语言的接口、生成可独立运行的iatlah程序、VC++调用Matlah程序、Matlab DotNet Builder与VC++,Matcom与C/C++以及VC++调用Matlab C++数学库。另外，附录中介绍有关动态链接库的基础知识。各章包含大量 的实例程序，可供寻求将Matlab程序脱离Matlab环境的Matlab程序设计人员、寻求在Matlab中调用C/C++程序的程序设计人员、寻求在C,C++中调用Matlab程序的程序设计人员学习和参考。(This book describes how to use Matlah mixed with the CC++ programming. Divided into 8 chapters. Including Matlab programming initially, Matlab compiler, Matlab and C language interface to generate can be run independently of the iatlah program, VC++ calls Matlah program, Matlab DotNet Builder and VC++, Matcom and C/C++ and VC++ calling Matlab C++ math library. In addition, the appendix describes the dynamic link library on the basis of knowledge. Each chapter contains a large number of example programs are available for the Matlab program from the Matlab environment, Matlab programmer, seeking to Matlab to call C/C++ program programmers seek to C, C++ calling Matlab program programmers study and reference.)

grid_world example with reinforcement learning