the set of neural network Genetic Algorithm

该程序通过遗传算法对图像进行聚类分析，并实现了图像的分类功能，分类效果明显较其他算法好-The genetic algorithm procedure for cluster analysis of the images and to achieve the classification of image features, the classification results significantly better than the other algorithms

针对现有遗传算法在多维非线性优选方面的不足，本文提出了一种基于小生境进化算法（NEA）的非线性优选模型，探讨了NEA算法的参数选择原则。通过大量仿真和比较，表明算法在复杂非线性优选中具有快速、高效、鲁棒性强的特点，并能在全局范围内有效搜索所有最优解。 -against existing genetic algorithms in three-dimensional nonlinear optimization for the shortage, the paper presents a niche evolutionary algorithm (NEA) nonlinear optimization model, the NEA on the parameters chosen algorithm principle. Through simulation and large, the algorithm shown in a complex nonlinear optimization is fast, efficient, robust features of the strong, and the global scope effective search all the optimal solution.

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

BP神经网络C语言程序包-BP neural network C language package

计算欧几里德距离C语言源代码,次源码经测试可有效运行,源码简单易懂-Calculation of Euclidean distance C-language source code, sub-source has been tested and can be effectively run, easy-to-read source

一类球型分布问题的实现-a ball-type distribution of achievement

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

一个机器人信息显示系统，代码比较全，你会喜欢的-Information display system of a robot, code-wide comparison, you would like

CCE is a multi-instance learning method solving multi-instance problems through adapting multi-instance representation to single-instance algorithms, which is quite different from existing multi-instance learning algorithms which attempt to adapt single-instance algorithms to multi-instance representation

遗传算法和神经网络的结合应用，通过Visual C++环境来实现该功能，使计算能力更强-Genetic algorithms and neural networks combination of applications, through the Visual C++ environment to achieve this feature, so that more computing power