Cukmojaza

神经网络图像处理程序，可以实现入门级功能(Neural network image processing procedures, entry-level functions can be achieved)

说明：  例题全部代码. 薛定宇, 陈阳泉. 高等应用数学问题的Matlab求解.(All the code examples. Xue Dingyu, Chen Yangquan. MATLAB to solve higher applied mathematical problems.)

image watermarking method

说明：  %solve u_xx + u_yy = f(x,y) % over the region D = [x0,xf,y0,yf] = {(x,y) |x0

copula函数 主要应用于BB7UgivenV_inverse2(copula function BB7UgivenV_inverse2)

matlab资料汇总，内含有基本使用手册以及详细的进阶过程，适合初学者以及放在手边随时使用(matlab data collected, as well as a detailed manual contains basic advanced course is suitable for beginners as well as at hand ready for use)

说明：  MATLAB 代码 第1章 BP神经网络的数据分类——语音特征信号分类 第2章 BP神经网络的非线性系统建模——非线性函数拟合 第3章 遗传算法优化BP神经网络——非线性函数拟合 第4章 神经网络遗传算法函数极值寻优——非线性函数极值寻优 第5章 基于BP_Adaboost的强分类器设计——公司财务预警建模 第6章 PID神经元网络解耦控制算法——多变量系统控制 第7章 RBF网络的回归--非线性函数回归的实现 ....等58章(MATLAB code The first chapter is BP neural network data classification -- speech characteristic signal classification The second chapter is the nonlinear system modeling of BP neural network nonlinear function fitting The third chapter, genetic algorithm optimization BP neural network - nonlinear function fitting The fourth chapter, neural network, genetic algorithm, function extreme value optimization nonlinear function extremum seeking The fifth chapter is based on BP_Adaboost's strong classifier design -- the company financial early-warning model The sixth chapter is PID neural network decoupling control algorithm multivariable system control The seventh chapter is the regression of RBF network the realization of nonlinear function regression .........the last is 58 chapters)

说明：  关于WS小世界的仿真，对于初学者来说可能比较有用·~· 大家可以看看 是用matlab编写的(WS small world on the simulation, might be useful for beginners ~* we can see is written in matlab)

说明：  这是一个盲源分离的应用程序，其中还包括做实验用的各种数据(This is a blind source separation applications, which also includes an experiment with the various data)

采用映射法降低ofdm系统分均比的matlab仿真程序(Ofdm systems using sub-mapping method to reduce the matlab simulation program than)