基于小波和神经网络的论文，来自期刊网

八数码难题求解程序本程序所采用的启发函数,本人不能证明它能解决任何的八数码难题,如果对于较难的问题会产生很多节点,有可能会使空间不足,所以尽量不要出太难的问题难为它.希望广大的人工智能爱好者能写出更好的启发函数.-eight digital problem solving program of the procedures used by the heuristic function, I can not prove that it can resolve any of the eight digital problem, if for the more difficult problems will have many nodes, it is likely to lack of space, so as not up to the difficult problems it hard. I hope the love AI who better to write a heuristic function better.

关于人工神经网络的实用书籍，适合初学者，以及有一定基础的读者学习-Information on artificial neural networks and practical books, suitable for beginners, as well as readers of a certain basis for learning

专家系统－神经网络代码-expert system-neural network code

这是通过A*算法实现8数码问题，求解可能出现的目标状态。通过回溯的方法实现对A*算法。不同于其它状态图的搜索实现8数码问题。-This is the A* algorithm eight digital issues, the solution may target state. Through the method back to the A* algorithm. Unlike other state diagram of the search to achieve eight digital problem.

谢大同郭涛算法(函数优化)源代码下载，和大家分享自己的东西-Xie Datong Guo Tao algorithm (function optimization) source code download, and share things on your own

简单的遗传算法的例子,对于遗传算法的初学者来说非常有用.-Simple genetic algorithm examples.it very helpful for the beginners of genetic algorithm

一个坦克大战的源码，适合VC初学者，希望对大家有所帮助！-a Tankedaizhan source code, suitable for beginners VC, and I hope to help you!

遗传算法，是模拟达尔文的遗传选择和自然淘汰的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法.遗传算法是一类可用于复杂系统优化的具有鲁棒性的搜索算法-Genetic algorithm, is a simulation of Darwinian natural selection to genetic selection and biological evolution of the computing model is a natural evolutionary process by simulating the optimal solution search methods. Is a kind of genetic algorithm can be used for optimization of complex systems is robust The search algorithm

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.

使用遗传算法求解邮递员问题，从而可以同时求得多个最优解-The use of genetic algorithm postman problem, which can be obtained at the same time more than the optimal solution