神经网络介绍的源码

BugBots是一个游戏，描述它的最好方式是它是一个测试人工智能的框架。 来源: http://sourceforge.net/projects/bugbots/-BugBots is a game, describing it as the best method is that it is a test of artificial intelligence framework. Source : http://sourceforge.net/projects/bugbots/

遗传算法初始化网络权值及其阈值参数 提高控制性能-Genetic algorithm to initialize the network weights and threshold parameters improve control performance

电梯程序，包含了上、下、停留等功能。还有关于优先级的设置。-Elevator procedures included upper and lower, to stay and other functions. Also on the priority settings.

一个简单实用的K紧邻分类器 -KNN

用贪心法解决TSP问题.输入数据可求得所要的最值的结果.-greedy algorithm used to solve TSP. Input data can be obtained by the most value to the results.

k-means算法(matlab编写),其中包含测试数据集,可以使用.-This is a k-means algorithm(in matlab) contains data set which can be used

人体运动检测与运动跟踪的源代码，用到了最新的算法，对这方面研究的人员很有帮助-Human Motion Detection and Motion Tracking source code, use the latest algorithms, this study helpful staff

遗传算法的文章- Heredity algorithm article

落煤残存瓦斯量的确定是采掘工作面瓦斯涌出量预测的重要环节,它直接影响着采掘工作面瓦斯涌出量预测的精度,并与煤的变质程度、落煤粒度、原始瓦斯含量、暴露时间等影响因素呈非线性关系。人工神经网络具有表示任意非线性关系和学习的能力,是解决复杂非线性、不确定性和时变性问题的新思想和新方法。基于此,作者提出自适应神经网络的落煤残存瓦斯量预测模型,并结合不同矿井落煤残存瓦斯量的实际测定结果进行验证研究。结果表明,自适应调整权值的变步长BP神经网络模型预测精度高,收敛速度快 该预测模型的应用可为采掘工作面瓦斯涌出量的动态预测提供可靠的基础数据,为采掘工作面落煤残存瓦斯量的确定提出了一种全新的方法和思路。-charged residual coal gas is to determine the volume of mining gas emission rate forecast an important link, which directly affect mining gas emission rate forecast accuracy, and with coal metamorphism, loading coal particle size, the original gas content, exposure time and other factors nonlinear relationship. Artificial neural networks have expressed arbitrary nonlinear relationships and the ability to solve complex nonlinear, time-varying uncertainty and the new ideas and new approaches. Based on this, the author of adaptive neural network loading coal residual gas production forecast model, and a combination of different loading coal mine gas remnants of the actual test results of research

经典的数据关联挖掘算法prefixspan-Classical data association mining algorithm PrefixSpan