用matlab编的，一个典型的BP神经网络的例子，相信对你的学习、工作都能起到一个参考的作用...

用于求解TSP问题的c++程序，已经试运行过-BasicAntColon AlgorithmC++forTSP

EBP反向传播算法对应的源程序，三层网络，分隐层神经元，输出层神经元，输入层神经元的一个基本的程序实现-EBP back-propagation algorithm the corresponding source code, the three-tier networks, sub-hidden layer neurons, output layer neurons, input layer neurons realize a basic procedure

文本识别是一个基础但却重要的课题，本算法给出其具体的实现过程。-Text identification is a basal but important task.This arithmetic give a material realized process.

ID3决策树算法的C语言实现-ID3 Decision Tree Algorithm in C language implementation

一个遗传算法程序-A genetic algorithm program

人工智能经典问题TSP问题的C语言实现，内含源程序和调试过程生成的文件-AI classic TSP"s C language, and includes source code debugging process produces files

大牛的神经网络支持向量机代码, 强烈推荐,实在太多，没办法,第三部分-The good program of SVM,Strong recommendation ,too long,no way,part three.

此代码经过大量使用，反映很好，代码优化，效率高-this code after extensive use, reflecting good code optimization, high efficiency! !

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

模式识别―分类程序VC源码,是《图像模式识别VC++技术实现》一书的源码-pattern recognition-classification procedure VC source, "VC image pattern recognition technology to achieve" a source book