人工智能的人工智能门户网站，很难找到，美国..

神经网络算法 神经网络算法 -BP networksBP networksBP networksBP networksBP networks

人工智能的教材啊- Artificial intelligence teaching material

遗传算法MATLB图像分割程序及例子，希望对大家有用-GA MATLB procedures, there are genetic algorithm selection, crossover and mutation function, some simple examples MABTLAB GA!

麻省理工大学遗传算法 -Massachusetts Institute of Technology Massachusetts Institute of Technology genetic algorithm genetic algorithm

使用遗传算法求解邮递员问题，从而可以同时求得多个最优解-The use of genetic algorithm postman problem, which can be obtained at the same time more than the optimal solution

这是一个研究人工智能中蚂蚁算法的研究性文档,对想了解这方面内容的人相当有益,很难才找到的-This is a study artificial intelligence ant algorithm research documents, to think about it as a very useful one, it is difficult to find the

统计学习理论是建立在一套较坚实的理论基础之上的，为解决有限样本学习问题提供了 一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比 如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法： 支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当 今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified framework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.

字符识别系统——利用双重用途分类后的神经网络系统对原始字符进行识别编码

本程序利用蚂蚁算法解决TSP（旅行商问题）问题-This procedure using ant algorithm to solve TSP (Traveling Salesman Problem) problem

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