Bayes-program

matlab在系统辨识中的应用此处为递推的极大似然法应用的源代码及运行后结果（包括图像）(Matlab system identification in the application of recursive here Maximum Likelihood of the application's source code and running after the results (including images))

一级倒立摆的系统仿真动画，初始加速度为0，实现最基本的小车不动，摆杆动的仿真程序(An inverted pendulum system simulation animation, initial acceleration is 0, the most basic car does not move, moving pendulum simulation program)

it is code for temporal feature extraction

实现信号的随机共振处理,包括matlab代码的实现(introduce the procedding of signal)

求出信号噪声的方差，对干扰信号的噪声的方差进行估测。( EVAR Noise variance estimation. Assuming that the deterministic function Y has additive Gaussian noise, EVAR(Y) returns an estimated variance of this noise.)

在matlab开发环境下实现m-shift图像目标跟踪算法，其中还有一篇比较好的参照论文(Development environment in the matlab m-shift image to achieve target tracking algorithm, of which there are a good reference paper)

基于ca cfar的恒虚警测试程序 基于ca cfar的恒虚警测试程序 基于ca cfar的恒虚警测试程序(Based ca cfar CFAR test program is based on ca cfar CFAR test program is based on ca cfar CFAR test program)

数字下变频器的matlab仿真，包括基带信号的调制，然后经过混频模块、CIC模块、FIR模块最后得到基带信号的过程。很多地方都批有详细的注释，很容易理解。(Digital down converter matlab simulation, including the modulation baseband signal, and then after mixing module, CIC module, FIR module finally get baseband signal process)

FKP authentication document

为了有效地应用遗传算法解决 鲁棒控制系统设计问题，将遗传算法与局部优化方法相结合，提出了基于降维扫描方法的自适应多目标遗传算法（DRSA-MOGA）。通过引入适应度函数标准化方法、基于最优Pareto解集搜索的降维扫描方法和适应度函数自适应调整方法，提高了算法的全局优化性能和局部搜索能力。仿真结果表明，DRSA-MOGA算法在不损失解的均匀度的情况下可以达到很高的逼近度(For effective application of genetic algorithms to solve robust control system design problems, genetic algorithms and local optimization method, based on reduced-order adaptive scanning method multi-objective genetic algorithm (DRSA-MOGA). Fitness function through the introduction of standardized methods, based on the Pareto optimal solution set of search methods and dimensionality reduction scan fitness function adaptive adjustment method, the algorithm improve the performance of global optimization and local search capabilities. The simulation results show that, DRSA-MOGA algorithm solution without loss of uniformity can be achieved under a high degree of approximation)