利用c语言实现人工智能神经网络的bam算法

基于遗传算法的图像模式识别 ,这个是本人收藏的有关遗传算法的源程序-based on genetic algorithm for image pattern recognition, this is my collection of the genetic algorithm source code

K-均值聚类算法的编程实现。包括逐点聚类和批处理聚类。K－均值聚类的的时间复杂度是n*k*m，其中n为样本数，k为类别数，m为样本维数。这个时间复杂度是相当客观的。因为如果用每秒10亿次的计算机对50个样本采用穷举法分两类，寻找最优，列举一遍约66.7天，分成3类，则要约3500万年。针对算法局部最优的缺点，本人正在编制模拟退火程序进行改进。希望及早奉给大家，倾听高手教诲。-K-means clustering algorithm programming. Point by point, including clustering and clustering batch. K-means clustering of the time complexity of n* k* m, n samples, several types of k, m sample dimension. The time complexity is a very objective. Because if we use one billion times per second the computer using 50 samples of two exhaustive method to find the optimal set out again about 66.7 days, divided into three categories, offering 3,500 years. Local optimal algorithm against the shortcomings, I was prepared simulated annealing process improvements. Early Feng hope for everyone, listen to the master teachings.

基本遗传学习分类系统，C++源代码-The basic classification system for genetic study, C++ Source code

一个很好用的自适应人工神经网络模型库，包括ADALINE/RBF/多层前向等，另外内含Demo，更易上手-A good use of adaptive artificial neural network model library, including the ADALINE/RBF/Multi-layer and so on, the other containing the Demo, even易上手

神经网络算法

基于小生境遗传算法的分类规则数据挖掘算法 -Based on Niche Genetic Algorithm for Classification Rules Data Mining Algorithm

人工智能中一个关于野人过河的小程序-AI Savage cross the river on a small procedure

　基于VB的遗传算法软件实现 在程序中,FitnessValue (i) 为适应度值数组、avFit2nessValue (100) 为归一化适应度值数组、Population2 Chrom(i ,j) 为遗传个体的等位基因值、Popsize 为种群中的个体数,CHROMLENGTH为一母体对的等位基因 总数。-VB-based genetic algorithm software implementation in the proceedings, FitnessValue (i) In order to meet an array value, avFit2nessValue (100) for the normalized fitness value of the array, Population2 Chrom (i, j) for the genetic value of individual alleles, Popsize for stocks in the number of individuals, CHROMLENGTH for one parent to the total number of alleles.

基于神经网络的乒乓球机器人乒乓球轨迹预测代码，神经网络的参数可能有待进一步改进-code of PingPong Robot trajectory predicting based on neural network

该系统使用极其简便，即使 你对各种网络模型不是很深刻的了解，也可以很好的使用该系统。使用时， 你可以自己修改网络的各种参数，交互性较好，而且该系统通过大量的图示 及参数设置，可以让你了解每个应用实例实现的过程及详细步骤。-The system is extremely easy to use, even if you have to various network model is not very profound understanding can be a good use of the system. When used, you can change the network"s own parameters, interactive better, but the system through a lot of icon and parameter settings, so you can understand each application process and the realization of detailed steps.