//开发平台：Microsoft .NET Framework 2.0，Microsoft Visual C# 2005 Express //日期：2005.3...

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

vs2005开发 遗传算法求解144城市的旅行商问题-VS2005 to develop genetic algorithm 144 city traveling salesman problem

采用“提前停止”方法提高 BP 网络的推广能力。对于和例 2相同的问题，在本例中我们将采用训练函数 traingdx -The use of

自己用JAVA编的人工智能的A*算法的八数码程序-own use JAVA series of the artificial intelligence of the A* algorithm eight digital procedures

基于matlab的指纹识别系统，采用基于细节点对比，输入图像必须是256×256，8－bit灰度值，500dip的指纹。-Matlab-based fingerprint identification system based on Minutiae contrast, imported images must be 256 x 256.8-bit gray scale values 500dip fingerprints.

可以用的gassy 一年前的源码源码源码-Can be used a year ago gassy source source source

模式识别的源代码，针对大样本集中样本自动分类的程序，可以手动控制输入点！-pattern recognition source code, sample concentration on large samples of automatic classification procedure can be controlled manually input!

很好的matlab模式识别工具箱，帮助大家建立典型的模式识别模式。-Good pattern recognition matlab toolbox to help the U.S. establish a typical pattern recognition model.

提供了遗传算法的一些代码库文件，国外下的很有用-provide the genetic algorithm code of the document, the foreign useful

线性神经网络应用实例-linear neural network application