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一种常用的关于信号稀疏度测试的指标，值得拥有。(A commonly used test for signal sparsity indicators, worth having.)

GLVQ neural network, this method is suitable for the two classification results of data processing

本文针对SLAM数据关联中使用最为广泛的最近邻方法作了改进，利用特征估计位置与载体预测位置之间的欧氏距离计算代替了全部特征与每个量测之间的马氏距离计算，避免了大量的矩阵乘法计算。该算法简单易行，降低了算法的计算复杂度，有利于SLAM算法的实时执行，且关联效果与全局最近邻法相同(In this paper, SLAM data association in the most widely used methods of improving the nearest neighbor, using the characteristics of the estimated location and the location of vector prediction Euclidean distance between the calculation instead of all the characteristics of each measurement between the Mahalanobis distance calculation, to avoid a large number of matrix multiplication calculation. The algorithm is simple, the algorithm to reduce computational complexity and is conducive to the implementation of real-time SLAM algorithm, and the effects associated with the same overall nearest neighbor method)

介绍了RHC控制算法的实现，有具体的例子(Introduced the RHC control algorithm, there are concrete examples)

倒立摆的模型和PID控制，Simmechanic 模型和控制(Inverted pendulum model and PID control, Simmechanic model and control)

matlab数学建模工具箱，约束优化,非线性规划。(matlab toolbox of mathematical modeling, constrained optimization, nonlinear programming.)

本书是久负盛名的C++经典教程引，其内容是C++大师Stanley B. Lippman丰富的实践经验和C++标准委员会原负责人Josée Lajoie对C++标准深入理解的完美结合，已经帮助全球无数程序员学会了C++。(Book is the most famous classic c++ tutorial, its content is c++ master Stanley b. Lippman wealth of practical experience and c++ standards committee of the original charge Josee Lajoie on c++ standard in-depth understanding of the perfect combination, has helped the world many programmers learned c++.)

说明：  mimo 中stbc 仿真程序 已验证成功(mimo in the simulation program has been successfully verified stbc)

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). )

Data-Driven Robust Approximate Optimal Tracking论文的实验示例程序。(Data-Driven Robust Approximate Optimal Tracking paper experimental sample program.)