线性M3网络及其示意图，用于检测M3网络冗余的算法

程序用C语言实现了贝叶斯在数据挖掘中分类和预测中的应用，通过程序可以很好的进行分类和预测。-This program use C language to realize the application of bayes in data mining and forecasting,you can use this program to make a good classification and forecasting

模式识别的源代码，针对大样本集中样本自动分类的程序，可以手动控制输入点！-pattern recognition source code, sample concentration on large samples of automatic classification procedure can be controlled manually input!

模拟遗传算法，JAVA-Simulation of Genetic Algorithms, JAVA

遗传算法编程很有用的，可以用来借鉴参考，希望对大家有帮助！-Genetic algorithm programming useful, can be used to draw reference to all of us want to help!

足球机器人避障算法，已通过实践证实可以用于足球比赛中，该源代码包含仿真界面，形象直观-Soccer robot obstacle avoidance algorithm has been proved through practice can be used for soccer matches, the source code contains the simulation interface, the image of intuitive

遗传基因算法源代码(资料)，C++源代码，是研究遗传算法的入门程序。-genetic algorithm source code (information), C source code, is to study the genetic algorithm entry procedures.

这还是一个蚁群算法实验室,不过内容要更为详细,看得更为清楚,可以用它来理解蚁群的整个觅食过程.-It is also a laboratory ant colony algorithm, but the content should be more detailed, more clearly seen, you can use it to understand the whole feeding process of ant colony.

使用模拟退火算法解决TSP问题，该程序中的模拟退火没有采用复杂的运算，实现了一个比较简单但运行高效的算法-using simulated annealing algorithm to solve TSP, the process of simulated annealing without the use of complex arithmetic, achieve a relatively simple but efficient operation of the algorithm

一个神经网络原型代码，针对的例子为《Perceptron Learning》 (Russell & Norvig， 第742页)-A neural network prototype code, for example for

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