FuenteVoz

maple起步教程，适合初学maple的人使用，可供相关人员参考。(maple started tutorial for beginners maple people use, for future study.)

matlab时频分析源代码 很好很强大。希望对您有用(matlab source code for a good time-frequency analysis is very powerful. Hope useful to you)

some cost function.fore example rastrigin,... .

对几何相似的图像进行相似部分的提取 从而进行分割提取(Similar to the geometry similar to some of the extracted image segmentation to extract)

非常好的--/---非常全/---数学手册/2007版(Mathematical manual/2007)

Please read THIS FILE

信噪比MATLAB程序，可以分析信号的干净程度， 适合信号处理的初学者。(SNR MATLAB program that can analyze the cleanness of the signal, signal processing for beginners.)

遗传算法的设计与实现 y=x.*sin(10*pi*x)+2 ( Genetic Algorithm Design and Implementation y=x.*sin(10*pi*x)+2 )

Constant k filters, also k-type filters, are a type of electronic filter designed using the image method. They are the original and simplest filters produced by this methodology and consist of a ladder network of identical sections of passive components. Historically, they are the first filters that could approach the ideal filter frequency response to within any prescribed limit with the addition of a sufficient number of sections. However, they are rarely considered for a modern design, the principles behind them having been superseded by other methodologies which are more accurate in their prediction of filter response.A filter in which the product of the series and shunt impedances is a constant that is independent of frequency.

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)