lvq neural network programming hope everyone can be on the

只要输入相关参数就可以得到结果，最值遗传算法，含源码-Just enter the relevant parameters can be the result, the most value genetic algorithm, with source

介绍了在LabView环境下的神经网络算法-Introduced in the LabView environment, the neural network algorithm

人工神经网络技术在智能分布式检索系统中应用- The artificial nerve network technology applies in the intelligent distributional retrieval system

关联模式的Apriori的vc实现，对数据挖掘感兴趣的人一定很希望得到吧！-VC implementation of Apriori based on Associate Pattern, is it what exactly you want in data mining?

模式识别中的十大经典算法,有详细的实现过程,花了很大工夫,现挥泪上传-Ten classic pattern recognition algorithms in detail the realization of the process and spent a lot of work, it is挥泪From

用模拟退火算法求解TSP问题-simulated annealing algorithm for TSP

这是一个将n个样本聚类到m个类别中 将n个样本在m个类里如何分配输出的遗传算法代码。 适应度：sum(i=1_110)sum(j=1_20)【xi-vj】~2-This is a cluster n samples to the m categories will be n samples in the m a class how to allocate the output of the genetic algorithm code. Fitness: sum (i = 1_110) sum (j = 1_20) [xi-vj] ~ 2

基于遗传算法的图案生成程序，其中有遗传算法的基本模块。-based on genetic algorithm design generation procedures, which are the basic genetic algorithm module.

该程序是基于BP网络的PID控制系统。系统由两部分构成：经典的PID控制器和神经网络。-that the procedure was based on BP network PID control system. System consists of two parts : the classic PID controller and neural networks.

模拟退火算法 模拟退火算法(Simulated Annealing,简称SA算法)是模拟加热熔化的金属的退火过程，来寻找全局最优解的有效方法之一。 模拟退火的基本思想和步骤如下： 设S＝{s1,s2,…,sn}为所有可能的状态所构成的集合， f：S―R为非负代价函数，即优化问题抽象如下： 寻找s*∈S，使得f(s*)＝min f(si) 任意si∈S （1）给定一较高初始温度T，随机产生初始状态S （2）按一定方式，对当前状态作随机扰动，产生一个新的状态S’ S’＝S＋sign(η).δ 其中δ为给定的步长， η为[－1,1]的随机数-simulated annealing algorithm (Simulated Annealing, or SA algorithm) is a simulation of heating molten metal in the annealing process, to find the global optimum one of the effective ways. Simulated Annealing basic ideas and the steps are as follows : S = (s1, s2, ..., sn) for all possible state posed by the pool, f : S-R non-negative cost function, that is abstract optimization problems are as follows : Find S* s, making f (s*) = min f (si) arbitrary si S (1) to set a higher initial temperature T, randomly generated initial state S (2) of a certain form, the current state of random disturbance, have a new state S "S" = S+ sign (). delta where given for the step, [-1,1] Random Number