这是个解决八码问题的程序，是外国人编的，用了图形处理技术，请大家参考...

一遗传算法的例子源程序-Example of a genetic algorithm source code

本程序实现的功能为建立一分类器,用于进行有监督的分类学习.-the program for the establishment of a functional classification, which is used for supervised classification study.

D星算法，是A星算法的动态版本，都是基本的路径规划算法-D Star algorithm, stars are A dynamic version of the algorithm is the basic path planning algorithm

用C撰写遗传基因法-C genes written law

该程序比较人性化，有个友好界面，能加载图像，并根据图像训练Hopfield神经网络，你也可以通过添加噪声给图像，以判断它的识别率。-Comparison of the program user-friendly, has a friendly interface that can load images, and in accordance with the training images Hopfield neural network, you can also add noise to images, to determine its recognition rate.

一本关于智能控制系统的好书，里面包括了模糊逻辑、专家系统、神经网络控制等内容，不仅理论叙述翔实，还有大量的实际程序附录，值得大家学习。-an intelligent control system on the books, including the inside of fuzzy logic, expert system, neural network and control content, not only informative narrative theory, there is a lot of practical procedures appendix, it is worth study.

这是一个银行模拟系统，对当今的银行进行模拟，它可以反映出银行的实际情况，非常有用哦，下来看看。-This is a simulation system banks, the Bank of today"s simulation, it can reflect the bank"s actual situation, a very useful Oh, look down.

用parzen窗方法，估计概率密度，采用高期核函数-With parzen window means of estimating the probability density function using high nucleus. . . .

神经网络中HOPFIELD模型算法的源程序，常用语控制领域！-Neural network model algorithm HOPFIELD source, commonly used to control the area of language!

围绕粒子群的当前质心对粒子群重新初始化.这样，每个粒子在随后的迭代中将在新的位置带着粒子在上次搜索中获得的“运动惯性”（wvi）向Pi，Pg的方向前进，从而可以在粒子群的运动过程中获得新的位置，增加求得更优解的机会.随着迭代的继续，经过变异的粒子群又将趋向于同一点，当粒子群收敛到一定程度时又进行下一次变异，如此反复，直到迭代结束.-particle swarm around the center of mass of the current PSO reinitialization. Thus, Each particle in the next iteration will be in the new location with particles in the last search was the "inertia" (wvi ) Pi, Pg orientation, and thus can PSO course of the campaign was a new position, increase seek better solutions opportunities. With the continued iteration, after variation of PSO will tend to the same point. When PSO converge to a certain extent when the next variation, so repeatedly, until the end of iteration.