约束条件下的序列模式挖掘（带约束）

基于svm的机器学习文本分类方法，具有很好的借鉴意义-Svm-based machine learning text classification methods, with a good reference

打开压缩包，其中的word文档中提供了“m个工件分配给m架机床的效益最优化问题，使用一种遗传算法解决。”的整个编程思路和过程分析。望有相关需要的朋友喜欢-open compressed, the word document provided "m m allocated to the workpiece-machine efficiency optimization, the use of a genetic algorithm to solve." The entire programming ideas and process analysis. Expect relevant to the needs of friends like

神经网络和遗传算法在水科学领域的应用-neural networks and genetic algorithms in the field of water science applications

IEEE中关于MRI脑图像模糊分割的相关文章，2007-2008年度最新-IEEE on MRI fuzzy segmentation of brain images related article ,2007-2008 annual update

林智仁编写的著名的SVM代码(libsvm-2[1]-林智仁prepared by well-known SVM code (libsvm-2 [1]

ID3算法有一定的自学习、自组织能力,适用于复杂系统的智能诊断。以实例验证了该算法对RH-KTB真空抽气系统故障诊断的有效性。-ID3 algorithm to a certain extent self-learning, self-organization, applied to a complex system of intelligent diagnosis. Examples to show that the algorithm of RH- KTB vacuum exhaust system fault diagnosis arrival.

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

svm training example from svm.org

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.

简要说明:基于c语言人工免疫源程序，附录在PDF文件中，与大家共享 -brief description : Based on the c language artificial immune source, the appendix in PDF files, and share