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说明：  在MATLAB上实现混沌时间序列的Volterra预测算法(in MATLAB achieve chaotic time series prediction algorithm Volterra)

matlab 经典例程里的数据分析与统计，对迅速掌握相关数学的matlab实现很有帮助(Matlab classic routines in the data analysis and statistics helpful to quickly grasp mathematics matlab)

聚类算法的KFMC算法，基于模糊矩阵的聚类算法。应用于空间聚类。(KFMC algorithm clustering algorithm, clustering algorithm based on fuzzy matrix. Applied to the spatial clustering.)

本代码主要实现了基于双字典交叉稀疏表示的SAR图像变化检测的功能。(This code mainly realize the function of SAR image change detection is based on the double cross dictionary sparse representation.)

利用optisystem软件仿真半导体激光器的P-I特性曲线。得到的图形遵循在超过阈值电流之后，输出光功率与电流成线性关系规律。(Using software optisystem simulation P- I characteristics of semiconductor laser curve.The graphics follow after more than threshold current, a linear relationship with the output light power and current laws.)

Kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping.

this code very good for TSOM

iteration de gauss dans le calcul du champ matlab modelisation electromagnétique les différence finies

Computes the circulant matrix of a vector.

包括kf，ekf，pf，upf可以自己定制模型参数，完成滤波(ReBEL currently contains most of the following functional units which can be used for state-, parameter- and joint-estimation: Kalman filter Extended Kalman filter Sigma-Point Kalman filters (SPKF) Unscented Kalman filter (UKF) Central difference Kalman filter (CDKF) Square-root SPKFs Gaussian mixture SPKFs Iterated SPKF SPKF smoothers Particle filters Generic SIR particle filter Gaussian sum particle filter Sigma-point particle filter Gaussian mixture sigma-point particle filter Rao-Blackwellized particle filters The italicized algorithms above are not fully functional yet (or included in the current release), but will be in the next or future releases. The code is designed to be as general, modular and extensible as possible, while at the same time trying to be as computationally efficient as possible. It has been tested with Matlab 7.2 (R2006a). )