人工智能学习的重要内容，对加强和巩固所学内容将起到非常重要的作用。-AI an important part of learning, to strengthen and consolidate the contents will play a very important role.

DBSCAN Designed by Dr. Michal aszykowski Department of Chemometrics Institute of Chemistry, The University of Silesia. It s the basic one.

solve the 8-puzzle problem using A* algorithm. Definitely written by my self, also include BGI graphics library output.-solve the 8-puzzle problem using A* algori thm. Definitely written by my self. NBS also include graphics library output.

神经网络的源程序，一个比较新的算法，分类效果较好！-neural network source, a relatively new algorithm, better classification results!

8。《遗传算法及其应用》，陈国良等编着 人民邮电出版社 2001年第一版 本书系统全面地介绍了遗传算法的基本原理、设计方法及其并行实现，以及它在组合优化、机器学习、图像处理、过程控制、进化神经网络、模糊模式识别和人工生命等方面的应用。 本书可作为高等院校计算机、无线电电子学、自动控制、生物医学工程等有关专业高年级学生或研究生的教材和参考书，也可供从事人工智能、信息处理研究和应用的科技人员学习参考。-8. "Genetic algorithm and its application," Chen Guoliang edited and published with the People"s Post and in 2001 the first book version of the full introduction of the genetic algorithm basic tenets , design methods and parallel implementation, and its portfolio optimization, machine learning, image processing, process control, evolutionary neural networks, Fuzzy Pattern Recognition and Artificial Life, and so on the application. The book can be used as institutions of higher learning computers, radio electronics, automatic control, biomedical engineering, and other professional grade students or graduate teaching and reference materials, are also available in artificial intelligence, information processing and application of information technology to study.

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

基于Hopfield神经网络解决旅行商问题，学习模式识别的朋友可以参考-Based on the Hopfield neural network to solve traveling salesman problem, pattern recognition learning can refer to a friend

neural network in Java

黑白棋子的算法程序实现详细步骤及实现过程-reversi son algorithm program and detailed steps for achieving

结合均匀设计表和小边经验公式产生初始种群，使TSP应用遗传算法能求出更优更快解，详尽说明见paper.doc。-uniform design combining a small side table and have empirical formula initial population, TSP application of genetic algorithms can be obtained faster and better solutions, detailed description of paper.doc.