Kohonen the SOFM (self

蚂蚁算法在服务质量方面的代码，主要是服务质量方面的东西-Ant Algorithm in the service quality of the code, mainly the quality of services of things

pso系统的编程，计算误码率的，经过仿真可以实际应用-PSO system programming, BER calculation, after the practical application of simulation can

基于java实现的遗传算法 神经网络 实数编码 交叉 选择 变异-achieve genetic neural network algorithm is coded cross-choice variant

用于m=2 的机器调度问题，可求出最优解与花费的时间-m = 2 for the machine scheduling problems, the optimal solution can be obtained with the time

这是一个nsga的应用问题。算法可能不是最优的。-application of nsga

基于canny的灰色预测和模式识别,标定的一种做法是：在一块平板上画若干个点， 同时用这两部相机照相，分别得到这些点在它们像平面上的像点，利用这两组像点的几何关系就可以得到这两部相机的相对位置。然而，无论在物平面或像平面上我们都无法直接得到没有几何尺寸的“点”。实际的做法是在物平面上画若干个圆（称为靶标），它们的圆心就是几何的点了。而它们的像一般会变形，如图1所示，所以必须从靶标上的这些圆的像中把圆心的像精确地找到，标定就可实现。-Based on the gray canny prediction and pattern recognition, calibration approach is to: a plate painted in a number of points, at the same time with these two cameras camera, respectively, as these points in their plane as points, the use of these two groups geometric relationship between the image point can be that the relative positions of two cameras. However, in terms of objects such as plane or plane, we can not directly be no geometry point. Actual practice is complex plane painted in a number of round (known as targets), their geometric center of a circle is the point. And they generally like deformation, as shown in Figure 1, it must target these round, like in the center of a circle of like to find precise, calibration can achieve.

遗传算法的实型表示实现，与大家共享一下。实际应用中只需改变evalF.m就可以-Genetic algorithm, said implementation of the real, to share with you about. Practical application can only change evalF.m

this is a program about TSP -this is a program about TSP

GENNET是一个通过遗传算法优化权重的16个神经元的全连接神经网络。训练目的是使网络在一定假设的激励下，随时间产生同目标函数同样的响应，即:可以把此网络制作为一个任意波形的信号发生器。 程序分3个示例说明遗传算法进化过程中网络输出的变化趋势。可以看出网络输出（蓝色曲线）随着时间变化逐渐逼近目标函数（红色曲线）。此程序不仅可以学习遗传算法，也可以用于研究全连接网络的直观示例。-GENNET is a genetic algorithm to optimize the weights by the 16 neurons fully connected neural networks. Training purposes is to enable network inspired by certain assumptions, over time, produce the same response with the objective function, namely: the network can be produced as an arbitrary waveform signal generator. Procedure, three examples illustrate the evolution of genetic algorithms in the network output trends. We can see that the network output (blue curve) changes over time, gradually approaching the objective function (red curve). This program can not only learn from the genetic algorithm can also be used to study visual example of a fully connected network.

遗传算法求解车辆路径问题，没有解压缩的密码。-Genetic Algorithm for vehicle routing problem, no extract passwords.