C词法分析器实现,AOE网络算法实现,KRUSKAL算法实现,PRIM算法实现,计算机图形学影线填充算法（键盘坐标输入）,计算机图形学影线填充算法（鼠标输入）,...

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

神 经 网 络 的 资 料-neural network data

GCP(图着色问题)的模拟退火算法应用源码-GCP (Graph Coloring Problem) application of the simulated annealing algorithm source code

RBF神经网络应用于IRIS数据集的例子-Am example of RBF-NN applied on IRIS Data Set

有关神经网络的源码-the neural network source

用遗传算法求解最短路径问题-Genetic Algorithm for the shortest path problem

模拟退火算法用于求解旅行商问题的matlab源程序-simulated annealing algorithm for solving the traveling salesman problem Matlab source

Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM regression. It also provides an automatic model selection tool for C-SVM classification. This document explains the use of libsvm.

MATLAB神经网络工具箱ＢＰ算法的比较，比较它们的训练时间长短以所达到的效果-MATLAB neural network toolbox BP algorithm compares the length of their training time in order to achieve the effect of the so

一个牛人的神经网络支持向量机程序,对于搞神经网络的朋友强烈推荐.第二部分-A wonderful program of svm code coming from a superman,and strong recommendation to someone who learn about pattern-recognition .The second part.