CHAPTER3

说明：  基于matlab的动态规划！非常好用！但运行比较麻烦(Matlab-based dynamic programming! Very easy to use! But running is too much trouble)

空时编码的matlab仿真程序，包括stbc、sttc和vblast，可以任意设定天线数，并有仿真图(Space_Time_coding in MATLAB,including sttc and stbc and vblast,we can set the numble of antenna in anyway.)

MUSIC算法和Capon算法的仿真程序，自己编写，注释详细，方便大家理解(MUSIC algorithm and Capon algorithm simulation program, their preparation, detailed notes, to facilitate understanding)

MatlaB与C++接口若干问题解析，详细讲述了Matlab与C++在对接时的关键点(MatlaB and C++ interface to resolve a number of issues in detail about the Matlab and C++ when the key points in the butt)

说明：  通过均衡器的RLS算法在MATLAB中实现消除传输信道的噪声干扰。(Through the equalizer in the RLS algorithm in MATLAB to achieve elimination of the transmission channel noise.)

GMDH_main为GMDH主函数； variable_Combin为输入层初始变量选为x1,x1^2,x1*x2,x2^2,x2时的输入变量矩阵值 variable_select计算X_train训练输入数据,Y_train训练输出数据,X_test测试输入数据,Y_test测试输出数据 Combin为求变量的两两组合 Sym_Combin为求符号变量的两两组合 PE_AIC求每层各神经元的参数估计W，及训练数据在参数估计后估计的输出out_train，测试数据在参数估计后的估计输出out_test，还有与实际比较的误差平方和PESS， 以及准则值AIC sym_representation求最终的输入输出符号表达式 Criterion_value求准则值 (GMDH_main main function for the GMDH variable_Combin initial variables chosen for the input layer, x1, x1 ^ 2, x1* x2, x2 ^ 2, x2 the value of the input variable matrix calculation X_train variable_select training input data, Y_train training output data, X_test test input data, Y_test for the sake of the test output data Combin pairwise combination of variables for the sake of symbolic variables Sym_Combin pairwise combinations PE_AIC neurons find each parameter estimation of the W, and after the training data is estimated in the estimation of output out_train, test data After the parameter estimates of the output estimated out_test, also compared with the actual sum of squared errors PESS, as well as the final criterion value AIC sym_representation seek the input and output values ​ ​ of symbolic expressions Criterion_value find criteria)

利用小波变换进行图像灰度增强，其中与直方图，中值滤波图像处理进行了对比(Using wavelet transform image gray enhanced, including histograms, median filter image processing were compared)

产生高斯白噪声，使用matlab编辑及仿真，均值为零(have a Gaussian white noise, editing and the use of Matlab simulation, mean zero)

采用PDE法进行灵敏度过滤，实现悬臂梁的拓扑优化(Sensitivity filtering using PDE method to realize topology optimization of cantilever beam)

利用KL散度进行效果评价的matlab仿真(KL divergence using impact evaluation of the matlab simulation)