dual_rpca

实现dft基本功能。对频率分辨率有特殊说明，希望对您有帮助。(The realization of the basic functions of dft. Frequency resolution of special note, I hope for your help.)

说明：  最小跳数路由算法实现节点更新,在原有的基础上随机散布网络节点，一定的时间内进行更新。(The update hop of MHC，the node will be randomed in the area,and in the right time,they will be updated.)

说明：  MATLAB 16QAM仿真调制与解调及其说明(MATLAB 16QAMdaskljdsajdiosaduiosamdklsajo)

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)

说明：  MATLAB CODE FOR INFORMATION THEORY AND CODING

BOC(2,1)的编码，BOC(2,1)的功率谱密度，可以完美运行，对做BOC编码的人很有用途(BOC (2,1) code, BOC (2,1) power spectral density, can run perfectly on the very people who make use of BOC coding)

马尔科夫链蒙特卡洛方法的一个例子，对初学者有用(An example of Markov chain Monte Carlo method, useful for beginners)

Elman neural network prediction Mackey-Glass serie

A Survey of Genetic Algorithms do autor M. Tomassini

一个应用于做人脸识别之前，对人脸来提取特征点的，带方向的滤波操作(wld is a filter to extract the important features from face image.)