Neural network model self

使用labview编写的神经网络程序，多输入多数出。-prepared by the use of PC neural network programming, multi-input from the majority.

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

基于神经网络的字母识别系统 -Letters based on neural network recognition system

SVM分类器，由上海交通大学编写，软件使用方便、好用-SVM classifier, from the preparation of the Shanghai Jiaotong University, the software easy to use, handy

本程序实现的是关联规则挖掘领域中规则发现算法.-this program is the Mining Association Rules field rules algorithm.

人工神经网络- Artificial nerve network

tsp问题，10个城市的最佳路径。。。。。。matlab-tsp problem, the best 10 cities in the path. . . . . . matlab

基于VB开发的粒子群优化算法的软件及源程序。-VB on the development of particle swarm optimization software and source code.

Mining sequential patterns with constraint (SPADE with constraint)

he algorithm is equivalent to Infomax by Bell and Sejnowski 1995 [1] using a maximum likelihood formulation. No noise is assumed and the number of observations must equal the number of sources. The BFGS method [2] is used for optimization. The number of independent components are calculated using Bayes Information Criterion [3] (BIC), with PCA for dimension reduction.