fuzzy-pid-control

利用Lattice Boltzmann Method计算方腔流动的matlab程序(Calculated using Lattice Boltzmann Method cavity flow in matlab program)

高信噪比实测数据线谱提取，使线谱提取的一种典型方法(high signal-to-noise ratio measured spectral line data extraction, spectral lines from a typical method )

kalman一维滤波，采用两点差分法确定初始值，并仿真出不同初值情况下的估计效果(the fiter base on kalman)

Code Matlab Cholesky vectors perturbed matrix, number of conditioning, matrix disturbed, normal matrix.

Regeneration: Perform Bit-plan slicing on the obtained high contrast pollen or test image for the image understanding and analysis or compression.

matlab实现fisher判别，对于统计方面有重要的应用，适合初学者学习(fisher discriminant matlab realize, for statistics has important applications, suitable for beginners to learn)

An implementation of Unscented Kalman Filter for nonlinear state estimation. (Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system. The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF. For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a)

networking work related to computer networks gives basic formula and examples to solve this

MATLAB 辛苦攒下的十大经典算法，这是M源码。(MATLAB algorithms classic source)

K-means++算法实现，加强了初始聚类点的选取（careful seeding）(K-means++ algorithm to enhance the selection of the initial clustering points (careful seeding))