matlab-for-SMART-CONTROL

独立成分分析,用fastICA算法来分离混合信号得到两个独立的原始信号(fastICA for estimating independent signal)

粒子群算法源程序，是近年来发展起来的一种新的进化算法。有实现容易、精度高、收敛快等优点。是一种并行算法。(Particle swarm algorithm source code, is a new evolutionary algorithm developed in recent years. There are easy to implement, high precision, fast convergence and so on. Is a kind of parallel algorithms.)

this is genetic algorithm source code for twolink flex manipulator

说明：  对OFDM系统中的基于循环前缀的符号定时同步进行了仿真。(On the OFDM system based on cyclic prefix for symbol timing synchronization is simulated.)

基带非线性器，最常用的基带非线性器是幂级数模型和限幅器模型，这里给出的是幂级数模型的。(Baseband nonlinear device, the most common is the baseband power series model and the nonlinear limiter model, where the model is given a power series.)

PRTool 5,matlab模式识别工具箱-PRTool 5 ,A Matlab Toolbox for Pattern Recognition(PRTool 5, matlab Pattern Recognition Toolbox-PRTool 5, A Matlab Toolbox for Pattern Recognition)

Lohmann3的一个全息图编码，能运行和出现正确的结果，含图(Lohmann3 a hologram coding, can run and the emergence of the correct result)

Compressive sampling is an emerging technique that promises to effectively recover a sparse signal from far fewer measurements than its dimension. The compressive sampling theory assures almost an exact recovery of a sparse signal if the signal is sensed randomly where the number of the measurements taken is proportional to the sparsity level and a log factor of the signal dimension. Encouraged by this emerging technique, this thesis briefly reviews the application of Compressive sampling in speech processing. It comprises the basic study of two necessary condition of compressive sensing theory: sparsity and incoherence. In this thesis, various sparsity domain and sensing matrix for speech signal and different pairs that satisfy incoherence condition has been compiled.

说明：  T通过粒子群算法进行优化，采用rastrigin函数进行测试的程序。(matlab code for PSO algorithm)

用MATLAB产生M序列作为系统的输入，根据传递函数，仿真输出，并得出输入的自相关函数和输入输出的互相关函数。(M sequence generated by MATLAB as input, in accordance with transfer function, simulation output, and come to enter the auto-correlation function and the input and output of the cross-correlation function.)