using artificial neural network XOR functions. With back

Matlab中的GA程序-Matlab GA procedures

新的神经网络算法源程序，很适合初学者，也可以在上面进行改进！-New neural network algorithm source code, it is suitable for beginners, you can make improvements in the above!

包含FuzzyKMeans, FuzzyGK, FuzzyR等算法，拓展了weka（本站可以下载）-Contains algorithm such as FuzzyKMeans, FuzzyGK, FuzzyR. Extends weka(it is can be download form this site)

完成基于遗传算法的图像模式识别功能流程描述-To complete the image based on genetic algorithms pattern recognition function of the process described

遗传算法神经网络，遗传算法神经网络遗传算法神经网络-genetic algorithm neural networks, genetic algorithms neural networks, genetic algorithm, neural network

Matlab遗传算法工具箱函数及应用实例 Matlab遗传算法工具箱函数及应用实例-Matlab genetic algorithm toolbox function and application of genetic algorithm Matlab toolbox function and application example

Hopfield Model Autoassociative Memory 源码, 经典的HOPFIELD人工神经网络例子源码-Hopfield Model Autoassociative Memory source, HOPFIELD classic example of artificial neural network source

关于多目标优化的代码，这个代码是一个框架，大家可以修改一下-Multi-objective optimization on the code, the code is a framework, we can make some corrections

介绍了神经网络模式识别的基本知识，适合于模式识别领域的人员-it introduces the ABC of the pattern recognation of NN, it is for the people of pattern recognation

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