osu_svm3.00

几路语音信号的盲源分离，采用FDICA，有图形化界面，特别适合中级学者(Several road blind source separation of speech signals, using FDICA, a graphical interface, especially for intermediate and scholars)

说明：  这个基于MATLAB的遗传算法的源程序，希望对有需要的同志提供帮助！(the MATLAB-based genetic algorithm source, and I hope to be in need of help comrades!)

matlab（7.x系列）从入门到精通一书的源码 包括结果截图 实用性很强(Matlab (7.x series) from entry to master a book source, including the results of screenshots Very practical)

说明：  使用3种方法（卡尔曼，巴特沃斯，小波）处理肌电信号，得出了关于那一种动作的各快肌肉参与情况。(The three methods (Kalman, Butterworth, Wavelet) were used to process the EMG signal, and the fast muscle involvement of that type of action was obtained.)

再高斯信道和瑞利信道下bpsk误码率的分析(Again Gaussian and Rayleigh channels channel bit error rate analysis bpsk)

相关向量机(RVM)的matlab源程序，包含快速算法，内含代码使用说明。 RVM采取是与支持向量机相同的函数形式稀疏概率模型，对未知函数进行预测或分类。 优点： (1) 不仅仅输出预测目标量的点估计值,还可以输出预测值的分布. (2) 使用更少数量的支持向量,从而显著减少输出目标量预测值的计算时间. (3) RVM不需要估计过多的参数. (4) RVM对是否满足Mercer 定理的核函数没有限制,适应性更好(Relevance Vector Machine (RVM) of the matlab source code, including fast algorithm that contains code for use. RVM support vector machine is taken the same functional form sparse probabilistic model to predict the unknown function or classification. Advantages: (1) is not only the amount of output predicted target point estimates, but also the distribution of predicted values ​ ​ can be output. (2) using a smaller number of support vectors, thereby significantly reducing the output target amount predicted value calculation time. (3) RVM does not require excessive parameter estimation. (4) RVM meets Mercer theorem on the kernel function is not limited, and better adaptability)

Matlab_曲面插值和拟合，dajie可以看看(something about Matlab_b-spline)

时间序列预测ARIMA模型，这是一种基于风速数据的预测程序。(ARIMA time series forecasting model, which is a program based on the forecast wind speed data.)

改变磁通直流调速，主要咋MATLAB中仿真(The weak magnetic speed regulating DC motor simulation source code)

用matlab打开即可，可以绘制功率谱的函数，十分方便(using Matlab can be opened, the power spectral mapping function, which is very convenient)