Based on the MATLAB neural network to complete the U.S. source

特别是在题库管理模块中对自动组卷功能常用的算法（包括随机选取法、回溯试探法和遗传算法）进行了比较。对于较流行的验证码技术也给出了具体实现。-particularly in the examination management module to automatically making up the algorithm commonly used functions (including random selection method, Retrospective testing and genetic algorithm) were compared. For more popular authentication code technology has provided a concrete realization.

简单遗传算法(SGA) 主要算法模块有:选择 交叉 变异 (三个遗传操作) 和 群体更新 -simple genetic algorithm (SGA) algorithm for the main modules are : choice of crossover and mutation (3 Genetic Manipulation) and update groups

dv-hop的定位算法，已知网络中几个锚节点就可以计算出网络中其他点的坐标，但这种算法有时由于网络密度的稀疏，可能误差会有一些-dv-hop positioning algorithm, known to the network a few anchor nodes on the network can calculate the coordinates of other points, but this algorithm is sometimes due to the sparse network density may be some errors

two Matlab functions for initializing and training a recurrent neural network -two Matlab functions for initializing and training a recurrent neural network

GEP（基因表达式程序设计）是一种新的演化算法，该文件是基本的GEP编程，采用功能的封装格式，测试通过，能够方便的使用。-GEP (Gene Expression Program Design) is a new evolutionary algorithm, which is the basic GEP programming, functional package format, through testing, to facilitate their use.

自编VC++遗传算法.改进后可应用其它方面.值得看看.-Self VC++ Genetic algorithm. Improved other aspects can be applied. Deserves to see.

bp神经网络 c语言程序-bp neural network c Language Program

关联规则中的频繁项集生成算法Mafia1.4版本，一个效果非常好的算法，可在linux和windows下编译运行。-association rules of frequent item sets Algorithm Mafia1.4 version, a very good effect algorithm, and the Linux compiler running under windows.

用人工神经网络实现异或的功能。用反向传播算法（BP）来实现神经网络，正确率可以达到80% -using artificial neural network XOR functions. With back-propagation algorithm (BP) to achieve neural network, can achieve an accuracy rate of 80%

Pascal Programs Printed in GENETIC ALGORITHMS IN SEARCH, OPTIMIZATION, AND MACHINE LEARNING by David E. Goldberg -Pascal Programs Printed in GENETIC ALGORITHMS IN SEARCH, OPTIMIZATION, AND MACHINE LEARNING by David E. Goldberg