用高斯和离子滤波来跟踪视频图像中的移动对象，和模式识别有关...

对股票走势的仿真，基于agent的设计思路-The trend of the stock simulation, agent-based design ideas

粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and ot

完整的3D 模型检索程序，使用C语言编写，可以使学习者从底层研究三维模型的检索框架，同时提供的C语言程序包也可以设计新的三维模型检索算法。-Complete 3D model retrieval, the use of C language, so that students can study three-dimensional model from the bottom of the search framework to provide the C language package can also be three-dimensional model of the design of the new search algorithm.

包括883分酒问题，图的遍历等人工智能基本算法-Including 883 hours of liquor problem, artificial intelligence, such as graph traversal algorithm

数据挖掘经典Apriori算法C源码实现-Classic Apriori data mining algorithm C source code to achieve

基于遗传算法的基元识别与提取，C++源代码-Based on genetic algorithm identification and extraction of primitives, C++ Source code

美国麻省理工学院出的遗传算法方面的好书.-Massachusetts Institute of Technology in the United States out of aspects of genetic algorithms books.

一个外国人写的字符识别训练源码，VC++源码，有源码、说明及演示可执行程序，对学习和研究模式识别很有帮助。-written by a foreigner Character Recognition training source, VC source, a source that, and the demonstration of the executable, on learning and pattern recognition research helpful.

Autolife模型是一个能够进行“开放式进化”的人工生命系统。每个Agent模型采用可以变化规则表长度的有限自动机模型建模。一方面Agent可以进行自我繁殖，同时模型中的选择机制没有采用显式的适应度函数而是采用能量消耗的简单模型而自发涌现出来，所以可以认为Agent模型是一个类Tierra系统。然而与Tierra、Avida等数字生命模型不同的是，Autolife模型进行了大大的简化，它界面友好，操作直接。虽然没有给每个Agent装配一个虚拟计算机，但是Agent与环境的耦合则可以看成一个图灵机模型，因此Agent可以通过变异而“任意”的编程序。通过Autolife模型，首先人们可以看到一般的生态系统中共存的现象：生物的大爆炸、大灭绝，Agent进化得越来越聪明；其次，用户可以通过变化不同的食物添加规则探索Agent与环境的关系；最后，如果允许Agent通过播种改变环境自动产生食物，那么组织的涌现就是一种不可避免的结果。Agent构成的组织具有自主运动的特性，还可以进行自我修复，可以说Autolife中的组织是一些真正的“活体”。-Autolife model is to conduct an "open-evolution" artificial life system. Each Agent can be used model changes the rules limited the length of the table automatic machine modeling. One Agent can be self-propagation, model selection mechanism not used Explicit the fitness function instead of using the energy consumption of a simple model to emerge spontaneously, so that the agent can be a model category Tierra system. But with Tierra, Avida digital life model is different Autolife model for greatly simplified its