said that the use of space rcc8 model spatial reasoning, problem solving csp. cs...

普通的pid控制数字化，可以使用效果好-ordinary pid control digital, you can use good results

这是一个二值hopfield神经网络源程序，有一个实例，训练数据保存在h7x8n4.trn,输出数据保存在ARCHIVE.LST中。-This is a value hopfield two neural network source, an example of training data stored in h7x8n4.trn, output data stored in ARCHIVE.LST China.

一个机器人信息显示系统，代码比较全，你会喜欢的-Information display system of a robot, code-wide comparison, you would like

遗传算法的matlab实现，举例说明遗传算法的基本实现步骤，适用于初学者-Matlab genetic algorithm to achieve, for example, shows the basic implementation steps of genetic algorithm is suitable for beginners

用9*9的矩阵显示字符0~9。 对BP网络进行训练后，可以识别带有一定噪声的字符。-Neural Nerwork。

用matlab语言编写的,该程序名为模拟退火算法m 源程序-Matlab language used, the procedure called simulated annealing algorithm m source

用于分类的一个工具箱，内含支持向量机、神经网络、主元分析、多变量样条等方法的matlab源代码，还有用户手册。-A tool for classification, including support vector machines, neural networks, principal component analysis, multivariate methods such as spline matlab source code, as well as user manuals.

利用PSO优化SVM，利用分组式训练方法提高算法速度-PSO to optimize the use of SVM, the use of packet-style training methods improve algorithm speed

数据挖掘算法 chameleon算法源代码- Data mining algorithm chameleon calculates the law origin code

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