神经网络基本算法源程序

使用最小二乘支持向量机对多维pyrim数据进行回归，需要下载最小二乘支持向量机工具箱。-use of least squares support vector machines for multidimensional data pyrim return need to download least squares support vector machines toolbox.

围绕粒子群的当前质心对粒子群重新初始化.这样，每个粒子在随后的迭代中将在新的位置带着粒子在上次搜索中获得的“运动惯性”（wvi）向Pi，Pg的方向前进，从而可以在粒子群的运动过程中获得新的位置，增加求得更优解的机会.随着迭代的继续，经过变异的粒子群又将趋向于同一点，当粒子群收敛到一定程度时又进行下一次变异，如此反复，直到迭代结束.-particle swarm around the center of mass of the current PSO reinitialization. Thus, Each particle in the next iteration will be in the new location with particles in the last search was the "inertia" (wvi ) Pi, Pg orientation, and thus can PSO course of the campaign was a new position, increase seek better solutions opportunities. With the continued iteration, after variation of PSO will tend to the same point. When PSO converge to a certain extent when the next variation, so repeatedly, until the end of iteration.

用粒子群优化算法计算给定图像的平滑度。

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.

apriori经典算法及一个改进算法的程序,里面有具体的解析与操作-classical apriori algorithm and an improved algorithm for the procedure, there are specific analytical and operational

这是一个我自己编写的JAVA程序，可以运行的。思想是将KMEANS和神经网络结合起来实现模式的分类。-This is a Java program writen by meself which can be run.The chief concept is to combine KMEANS and NN to achieve classification of pattens.

非常简单的一个神经网络小程序 自己写的 还用了遗传算法进行优化-Very simple neural network of a small program written in their own also used genetic algorithms to optimize

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

实现模糊控制算法(AD DA)和人机接口（4x4键盘 2x16LCD 串口1/2）

模式识别的书，介绍的论文模式识别的书，介绍的论文-Pattern Recognition book on pattern recognition paper book, the thesis introduction