云模型matlab源代码,下载后可以直接使用

jnbc与weka的接口，包含src和bin文件-jnbc weka and the interface, and contains src paper bin

为遗传算法提供基类，该基类将评价函数值直接作为适合度，采用新个体直接替换老个体的整体再生法 -genetic algorithm to provide the base class, the base class will be evaluated as a direct function of fitness, Individual direct new replacing the old individual"s overall regeneration

神经网络理论与Matlab7电子工业出版社原代码-neural network theory and Matlab7 Electronic Industry Publishing House source

模糊K近邻分类器，模式识别和机器学习经常要使用的一个分类器-K neighbor fuzzy classifier, pattern recognition and machine learning are often used in a classifier

神经网络算法

用于机器人的识别路程的优化,用与其中，去得很好的结果-For the robot to identify the optimal way to use and which to very good results

--- --- --- -人脸检测----------------------Face Detection

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

这是一篇步态识别和检测的文章：这是在视频流的基础上，区别于以往的静态图像-This is a gait recognition and detection of the article : This is the video stream on the basis of distinguished from the previous static images

很好的matlab模式识别工具箱，帮助大家建立典型的模式识别模式。-Good pattern recognition matlab toolbox to help the U.S. establish a typical pattern recognition model.