MultipathDopplerEffects

说明：  这是一个关于遗传算法的m文件,不错 o (This is about the genetic algorithm m files, henbucuo o)

超宽带脉冲形成技术，等效为切比雪夫逼近理论的脉冲产生算法研究的matlab仿真代码(UWB matlab)

自适应波束形成方向图，G-S正交化算法等(Adaptive beamforming pattern, GS orthogonalization algorithm)

matlab中的关于图像的函数大全，对于 新手是一个很好的词典！！！(all the image functions of matlab)

BPSK信号功率谱密度的MATLAB编程代码，运行得到波形。(BPSK signal power spectral density of the MATLAB programming code that runs waveform.)

AS1500CD user manual

用C编写的地震资料楔形体模型，可以变换参数，简单通用(Written using C seismic data wedge model, may come and go parameters, simple and universal)

计算机仿真用于主成分分析的ICA分析程序，算法举例和波形分析程序。(Computer simulation for analysis of principal component analysis of ICA procedures, algorithms and waveform analysis program for example.)

用Matlab的GUI界面设计滤波器人机界面，可选FIR、IIR等高通，低通，带通，带阻滤波器，手动设定好参数，可直观看到幅频特性曲线和相频特性曲线。(Using Matlab GUI interface design a filter interface, optional FIR, IIR high pass, low pass, band pass, band stop filter, the parameters are set manually, you can visually see the amplitude-frequency characteristic and phase-frequency characteristic curve .)

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域(Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications)