Is a collection of various commonly used algorithms and recursive, neural networ...

BLDC建模及神经网络控制，建模讲述的很详细，难得的论文。-BLDC modeling and neural network control, modeling, described in great detail, rare papers.

改进的量子搜索算法的论文，用于模式识别等方面-Quantum search algorithm to improve the paper for pattern recognition, etc.

包分类算法Hypercuts算法，包分类算法Hypercuts算法-Hypercuts

nsga2非支配排序遗传算法，c++源码实现-nsga2 non-dominated sorting genetic algorithm, c++ source code to achieve

用于机器人的识别路程的优化,用与其中，去得很好的结果-For the robot to identify the optimal way to use and which to very good results

关于遗传算法 源代码-On the genetic algorithm source code

五子棋是一种受大众广泛喜爱的游戏，这里介绍五子棋程序的数据结构、评分规则、胜负判断方法，重点分析了搜索算法，并在传统的博弈算法在五子棋应用中进行一些改进，从而使剪枝更有效，运算性能更好。改进包括：不使用closed表；改变棋盘搜索顺序；增加记录最大棋盘信息的指针。实验证明，这几点改进对提高效率有很高帮助。

提出了一种利用S函数实验结果表明：ICA可以将 脑电信号中包含的心电（ECG）、眼电（EOG）等多种干扰信号成功地分离出来-use of a S-function experimental results show : ICA EEG can be included in the heart (ECG), eyes (EOG) and other interference signal successfully separated

本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,

此文档是遗传算法原理加源代码。 生物的进化是一个奇妙的优化过程，它通过选择淘汰，突然变异，基因遗传等规律产生适应环境变化的优良物种。遗传算法是根据生物进化思想而启发得出的一种全局优化算法。 -This document is the principle of genetic algorithm source code increases. Biological evolution is a wonderful optimization process, it eliminated by choosing a sudden variation of genetic and other changes in the law to adapt to the environment arising from the fine species. Genetic algorithm is based on ideas of biological evolution and the inspiration derived from a global optimization algorithm.