用于求解多目标优化问题，多目标进化算法源程序

人工智能与模糊控制_电力系统-artificial intelligence and fuzzy control _ Power System

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.

这是好用的源代码-This is the source code handy

FisherFace, an algorithm based on PCA and LDA

关于演化算法的小程序.以一个二元二次方程的求极值为例.对输入参数作了简化的固定处理.较好的说明了演化算法.可直接运行.-on evolutionary algorithm small procedures. A binary quadratic equation for the extreme example. To simplify the input parameters fixed address. A good note evolutionary algorithm. Direct operation.

给出了FIR滤波器设计的C语言程序，可以根据设计要求，灵活地调整参数，得到需要的滤波器参数。-given FIR filter design in C language program, according to the design requirements, flexibility to adjust parameters and the filter parameters.

文中设计了一个3层径向基神经网络(RBFN)用于对企业的5项评价指标进行聚类分析，并与蚁群算法做了比较分析。RBFN由输入层 到隐含层采用传统的K一均值算法，隐含层到输出层通过“模2递减”学习速率的BP学习；蚁群算法根据信息素的分配能够自动调整收索 路径，从而达到数据自动聚类的目的。结果表明，与蚁群算法相比，改进RBFN具有快速收敛、自动识别奇异样本的优点，而蚁群算法 无须教师学习，并能够达到全局最优。-In this paper, we designed a 3-layer RBF neural network (RBFN) for the 5-to-business evaluation indicators cluster analysis and ant colony algorithm has done a comparative analysis. RBFN from input layer to hidden layer using the traditional K-means algorithm, hidden layer to output layer through the Mode 2 decreasing learning rate of BP learning ant colony algorithm based on pheromone can automatically adjust the allocation of land Faso path, thereby to achieve the purpose of automatic data clustering. The results showed that compared with the ant colony algorithm to improve the RBFN has a fast convergence, automatic identification of singular advantage of the sample, while the ant colony algorithm do not need teachers to learn and be able to reach the global optimum.

随机神经网络工具箱-random neural network toolbox

ADALINE(神经网络 vc＋＋编写的)-ADALINE (neural networks, prepared by vc )

通过C4.5 的实现可以进行构建决策树 来进行有效的分类-Through the realization of C4.5 decision tree can be constructed to carry out an effective classification