我的八数码游戏！！ 学人工智能的编写的

高斯混合模型C++版本-Gaussian mixture model C version

独立成分分析的批数据处理算法JADE,计算量虽然大些，但至今仍被人采用-Independent component analysis of batch data-processing algorithm JADE, although the calculation of the volume, but is still used

八数码难题求解程序本程序所采用的启发函数,本人不能证明它能解决任何的八数码难题,如果对于较难的问题会产生很多节点,有可能会使空间不足,所以尽量不要出太难的问题难为它.希望广大的人工智能爱好者能写出更好的启发函数.-eight digital problem solving program of the procedures used by the heuristic function, I can not prove that it can resolve any of the eight digital problem, if for the more difficult problems will have many nodes, it is likely to lack of space, so as not up to the difficult problems it hard. I hope the love AI who better to write a heuristic function better.

用Java编的人工智能的归结反演算法-Java series attributed to the anti-artificial intelligence algorithms

变尺度法的源程序，用于优化中的非线性规划，效还可以的。-variable scale method of the source for the optimization of nonlinear programming, the effect can be.

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

自动发贴机器人，可用于多个论坛的哦。能够突破房SPAM系统 。-Automatically post robot can be used in a number of forums Oh. Able to break through the Housing SPAM system.

BP神经网络的基本算法. 基于对话框-BP neural network algorithm. Based on the dialog box

Basic library that implements Kohonen s SOM and its learning. Lanuage: C# (.Net 3.5 Framework)

神经网络C语言源程序-neural network C language source