Cyclic Spectral Correlation

一段关于捷联惯导系统卡尔曼滤波的Matlab仿真程序，(Paragraph on the SINS Kalman filter Matlab simulation program,)

三相并联有源电力滤波器，基于瞬时无功理论的ip-iq电流检测法，三角波比较控制，抑制谐波电流(Three-phase shunt active power filter based on instantaneous reactive power theory, ip-iq current detection method, the triangular wave comparison control, inhibition of the harmonic current)

说明：  通过不同大小的窗口移动，计算每个窗口内地形剖面数据的均方根值，得到的数据在双对数图中拟合直线可求得分维数(move the windows of different size and calculate the rms of the data in the window.Then using the resultant data to plot,so that the fractal dimension can be gotten.)

KNN是常用的一个对于大维数据分类的方法，大家可以参考下(KNN classify)

matlab实现文件读取，波形处理，小波变换，文件保存。(use matlabe to read and write files, to process wave and to save files)

matlab遗传算法matlab遗传算法matlab遗传算法matlab遗传算法(matlab genetic algorithmmatlab genetic algorithmmatlab genetic algorithmmatlab genetic algorithm)

说明：  matlab编写的相同要求时提某列取大小值。(Matlab prepared by the same requirements when a series from a size value.)

说明：  粒子滤波算法与扩展卡尔曼滤波算法的比较方法。(Particle filter and extended Kalman filter algorithm for comparison.)

some example for matlab

独立成份分析（ＩＣＡ）以及ｗｉｎｎｅｒ滤波 Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S + N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...( Source separation of complex signals with JADE. Jade performs `Source Separation in the following sense: X is an n x T data matrix assumed modelled as X = A S+ N where o A is an unknown n x m matrix with full rank. o S is a m x T data matrix (source signals) with the properties a) for each t, the components of S(:,t) are statistically independent b) for each p, the S(p,:) is the realization of a zero-mean `source signal . c) At most one of these processes has a vanishing 4th-order cumulant. o N is a n x T matrix. It is a realization of a spatially white Gaussian noise, i.e. Cov(X) = sigma*eye(n) with unknown variance sigma. This is probably better than no modeling at all...)