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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.

模式识别源程序，希望大家能够喜欢，配套书籍是模式识别-Pattern recognition source code, hope that we can like it, matching books are Pattern Recognition

神经网络C程序的第8章-neural network C procedures for the Chapter 8

为了测试评估贝叶斯分类器的性能,用不同数据集进行对比实验是必不可少的. -In order to test the assessment of the performance of Bayesian classifier, and compare different experimental data sets is essential.

遗传算法简称GA(Genetic Algorithm)，在本质上是一种不依赖具体问题的直接搜索方法。遗传算法在模式识别、神经网络、图像处理、机器学习、工业优化控制、自适应控制、生物科学、社会科学等方面都得到应用。在人工智能研究中，现在人们认为“遗传算法、自适应系统、细胞自动机、混沌理论与人工智能一样，都是对今后十年的计算技术有重大影响的关键技术”。-Referred to as genetic algorithm GA (Genetic Algorithm), in essence is a kind of do not rely on specific issues of direct search method. Genetic algorithm in pattern recognition, neural networks, image processing, machine learning, optimization of industrial control, adaptive control, biological sciences, social sciences and so on have been applied. Research in artificial intelligence, now that the " genetic algorithms, adaptive systems, cellular automata, chaos theory and artificial intelligence, are the next ten years of the computing technology have a major impact on the key technology."

一个关于BP网络的设计，实现一个函数的逼近，了解关于MATLAB神经网络工具箱的应用。-On the design of BP network, the realization of a function approaches, learn more about the MATLAB neural network toolbox applications.

机器博弈机器搜索算法的研究，做acm与人工智能的可以参考下-Machine Game Machine search algorithm research, acm to do with artificial intelligence can refer to the next

人工神经网络技术在智能分布式检索系统中应用- The artificial nerve network technology applies in the intelligent distributional retrieval system

扩展遗传算法SPEAII(Strength Paretor Evaluation Algorithm)算法的代码实现,良好的程序框架，便于向其他应用领域扩展，建议大家使用。-Extended genetic algorithm SPEAII (Strength Paretor Evaluation Algorithm) algorithm code, a good procedural framework to facilitate expansion to other application areas, it is recommended the use of everyone.

LS-SVM_UserGuide是LS-SVM使用方法介绍的一篇文章，竟对我们学习支持向量机有很大的帮助。-LS-SVM_UserGuide is the use of LS-SVM method described in an article we had on the support vector machine learning of great help.