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说明：  Matlab中非图像工具箱的生成拉普拉斯滤波器的源程序。(Matlab Toolbox African Image Generation Laplace filter of the source.)

用MATLAB编写的 LSB 信息隐藏软件(MATLAB LSB prepared by the Software Information Hiding)

This Matlab source code is for paper LEACHC for wireless sensor networks.

基于matlab的ofdm源代码合集。包括simulink和c所写代码。(Ofdm matlab source code based on the collection. Including simulink and c written code.)

MATLAB下的小波变换图像处理代码，采用HAAR函数实现，含HAAR函数代码，希望有点用(MATLAB Wavelet Transform in image processing code, using HAAR function implementation, including HAAR function code, with a little hope)

Decldata source code for mac system 7 - resource files

说明：  极限学习机是一种神经网络仿真，比BP,SWM学习速度更快，参数设置更容易(Extreme Learning Machine is a kind of neural network simulation, compared with BP, SWM learning faster, easier to set parameters)

说明：  Study of an energy conversion chain wind based on a AEROTURBINE

异步电动机直接转矩控制基本原理和基于dsp控制(Induction motor direct torque control principle and control based on dsp)

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)