SOMSUANFAMATLAB3

Among the high-complexity methods, two methods dominate the literature Kalman filtering and Mixture of Gaussians (MoG). Both have their advantages, but Kalman filtering gets slammed in every paper for leaving object trails that can t be eliminated. As this seems like a possible deal breaker for many applications, I went with MoG. Also, MoG is more robust, as it can handle multi-modal distributions. For instance, a leaf waving against a blue sky has two modes—leaf and sky. MoG can filter out both. Kalman filters effectively track a single Gaussian, and are therefore unimodal: they can filter out only leaf or sky, but usually not both.

matlab description for GA optimization

说明：  k均值分类 效率高 代码简单 利用最小均方误差准则编写(k-means efficient)

基于单目视觉的单目标跟踪情部下meanshift算法与kalman滤波相结合的程序。(Monocular vision-based target tracking situation of single men meanshift algorithm and kalman filter combined procedure.)

ldpc的matlab实现程序，和几篇文档资料(ldpc matlab implementation procedures, and several documentation)

说明：  狼群算法基础原始程序，内含对应的2014年狼群算法原始论文(The basic original program of Wolf pack algorithm, including the corresponding original paper of 2014 Wolf pack algorithm)

cutting force calculation with coefficients

This file contain Frequency hoping techniques for the communication

改程序可以进行奇异值分解。是一种压缩后的奇异值分解。也可以进行广义奇异值分解。DSVD Damped SVD and GSVD regularization.(Program can change the singular value decomposition. Is a compressed singular value decomposition. Can also be generalized singular value decomposition. DSVD Damped SVD and GSVD regularization.)

this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the hidden states of a nonlinear, non-Gaussian state space model.(this demo is to show you how to implement a ge neric SIR (a.k.a. particle, the bootstrap. Monte Carlo) filter to estimate the hidden stat es of a nonlinear. non-Gaussian state space model.)