face recognition test source code

神经网络与pid混合控制源程序，可以很好的解决非线性控制问题-Neural network and hybrid control pid source can be a very good solution to nonlinear control problems

遗传算法 算法实例MATLAB程序，包括了：选择 变异 交叉 遗传算子-GA

Fisher判别，用于模式识别的Fisher线性判别-Fisher Discriminant for pattern recognition Fisher Linear Discriminant

人工智能中的广度搜索的九宫问题 用VC实现-breadth of artificial intelligence search JiuGongTu issue with VC

关于apriori算法的一个小程序，用于数据挖掘关联规则，呵呵．-apriori algorithm on a small procedures for data mining association rules, Ha ha.

基于遗传算法改进的稀疏分解算法，已调试过了，写论文是编写的-Based on genetic algorithm to improve the sparse decomposition algorithm, has been testing and writing papers are prepared

英国svm编写的环境用vc++开发，有完整的源代码，一定会有帮助！

机器学习讲义，内容丰富，讲解详细清楚，很全面积。-Machine Learning lecture, rich in content, clearly explaining in detail, it is the whole area.

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