SOM算法,matlab程序,是经典的教科书的例子程序

经典的SOM人工神经网络例子源码.zip 经典的SOM人工神经网络例子源码.zip-classic example of artificial neural network source. Zip SOM classic example of artificial neural network source. Zip

其中包括模拟退火算法的具体实现，还有其他经典算法的实现，数模全赛逢山开路问题的解答程序-Including simulated annealing algorithm to achieve the specific, there are other classical algorithm for the realization of digital-analog-wide open tournament every hill questions procedure

Wavelets, Approximation and Statistical Applications-Wavelets. Approximation and Statistical Applications

该源码用来实现基本粒子群的算法，值得参考-The source algorithm is used to achieve the elementary particle group, worth considering

关于遗传算法的算法-on Genetic Algorithms Algorithm

Counterpropagation Network Vision 源码, 经典的CPN人工神经网络例子源码-Counterpropagation Network Vision Source, a classic example of artificial neural network CPN-source

聚类分析简介，是个课件，希望对大家有帮助。-Introduction Cluster analysis is a courseware, we want to help.

libsvm在matlab中的应用。把libsvm嵌入matlab中调用。-libsvm in matlab application. Embedded matlab to call libsvm.

本程序能实现专家PID控制。专家控制的实质是基于受控对象和控制规律的各种知识，并以智能的方式利用这些知识来设计控制器。-the procedures to achieve expert PID control. Experts control of the controlled substance is based on the object and control the various law knowledge, and the intelligent use of this knowledge to design controller.

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