一个机器学习算法软件包,包括神经网络,模糊逻辑,支持向量机,采用MATLAB平台实现,...

多层神经网络范例 http://www.codeproject.com/cpp/MLP.asp?df=100&forumid=148477&exp=0&select=1141594#xx1141594-multilayer neural network model http :// www.codeproject.com/cpp/MLP.asp df = 1

粗糙集属性约简算法程序和相应的学术文章,以及一些参考资料和学习网站-Rough Set Attribute Reduction Algorithm procedures and the corresponding academic article, as well as some reference and learning site

用MATLAB编写的支持向量机，实现了线性可分与线性不可分的情况，还有非线性支持向量机，里面部分常用的核函数。-Prepared using MATLAB support vector machine, to achieve a linearly separable and linear indivisible, it is still non-linear support vector machine, which part of the kernel function used.

遗传算法解决双变量的函数最优化问题，有按钮的界面，用bc所编，生动模拟遗传进化过程-genetic algorithm to solve the two- variable optimization function, the button interface, using bc prepared by the vivid simulation of the process of genetic evolution

粗糙集遗传算法例程-Rough Set routines Genetic Algorithm

More AI...(GEP) Gene Expression Programming in C# and .NET 由Michael Gold用C#开发的GEP程序.-AI ... (GEP) Gene Expression Programming in C# and. By Michael Gold NET C# development of the GEP procedures.

游戏编程AI源码，包括A*等findpath的算法-AI game programming source code, including the A* algorithm, such as findpath

神经网络C语言源程序 绝对经典。 ADALINE,ART1,BAM,BOLTZMAN,BPN CPN,hopfield, som, -neural network C language source absolute classic. ADALINE, ART1, BAM, BOLTZMAN, CPN 7.82, hopfield, som.

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

clusterfilesrev 最新的分类聚类代码，delphi编写-err