来自于www2.cs.uregina.ca的c4.5源码

使用于初级使用者.能够直接使用采用最优化算法 遗传算法-use in the primary users. To use the most direct use of genetic algorithm optimization algorithm

一个蚁群算法，大家很想要得东东，共享给大家了。-an ant colony algorithm, we want to fine Eastern, we had to share.

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

是一个非常全面的pso的程序，有16个函数的测试，并且用三维图形将寻优过程显示出来。对所有的参数都可以修改测试-Is a very comprehensive procedure for the PSO, there are 16 function testing, and using three-dimensional graphics optimization process will be displayed. For all the parameters can modify the test

这是一个基于WEB的树木分类专家系统asp源代码,对于初学者特别有帮助!-This is a Web-based expert system for classification trees asp source code, especially for beginners help!

神经网络hopfield源码- Nerve network hopfield source code

差分进化算法的一篇全国优秀硕士论文，目前国内对此的研究还处于初步阶段-Differential Evolution of a national excellent master

指纹识别程序，大家看看吧，也许有用，不用客气-fingerprint identification procedures, we look at it, perhaps useful, not polite

神经网络训练程序，用来进行RBF网络的权值训练，利用训练好的权值可以进行自适应滤波！-neural network training procedures used for RBF network weights training. good use of training weights can be adaptive filtering!