VC中利用人工智能解决八迷宫问题。这是一个很有趣的人工智能小程序，不妨试试看。...

vb写的改进的遗传算法程序。是一些通用代码，与具体问题对应的代码空出来，用的时候填进去。根据我的经验，要想用好遗传算法，代码必须自己写，因为问题不同，编码，杂交，变异算子可能都不一样。特别是杂交算子，需要根据问题调整，甚至创造出新的杂交方法。而且适应度计算的代码也需要自己写，特别是带约束的优化问题 -vb write improvement in the genetic algorithm. Some common code, with the specific issues the corresponding code space, and the time spent filling into account. According to my experience, in order to make good use of genetic algorithms, code must write their own, because different issues, coding, hybridization and mutation operator may not like. In particular hybrid operator, the need to adjust the problem, or even create a new hybrid method. But fitness calculation of the code required to write their own, especially with the constrained optimization problem

本程序能实现专家PID控制。专家控制的实质是基于受控对象和控制规律的各种知识，并以智能的方式利用这些知识来设计控制器。-the procedures to achieve expert PID control. Experts control of the controlled substance is based on the object and control the various law knowledge, and the intelligent use of this knowledge to design controller.

人工神经网络BPN，人工神经网络BP算法源代码-Artificial neural network BPN, artificial neural network BP algorithm source code

模式识别分类 内有聚类算法.分段线性算法-Categorization of pattern recongnization includes clustering algorithm and segmentation linear algorithm.

bp网络代码 VC编写，BP算法的过程及注释说明-bp code VC network to prepare, BP algorithm for the process and Notes

该程序介绍了一种简单的遗传算法应用程序，采用matlab高效编程，适合于二进制编码的典型应用。-The program introduced a simple application of genetic algorithm using matlab efficient programming, suitable for the typical application binary code.

遗传神经网络-Genetic Neural Network

标准PSO（粒子群）算法，matlab版-standard PSO (PSO) algorithm, Matlab version

人脸检测源代码,是人工智能检索方面比较常用的,大家下班啊-Face Detection source code, artificial intelligence search area is relatively common, we work ah

统计学习理论是建立在一套较坚实的理论基础之上的，为解决有限样本学习问题提供了 一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比 如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法： 支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当 今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified framework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.