Weka机器学习系统（网站下载），一个简化的再发展…

基因表达式编程 基本算法 能够完成术用于公式发现、函数挖掘，关联规,则挖掘，因子分解，和预测等-gene expression programming algorithm can be used for the completion of the formula found that function Mining, related regulations, mining, factorization, and the forecast

用VC++编写的遗传算法源程序，可以用内容较多-very useful, widely,VC++, coding, Genetic algorithm of C++

用神经网络算法求解tsp问题。。。matlab编写。附有城市坐标用于检验。 -Using neural network algorithm for solving the problem tsp. . . matlab prepared. Coordinate with the city for inspection.

人工神经网络bp算法的matlab实现，对bp算法的matlab实现进行了简要的试探-realizing bp arithmetic of neural networks by matlab and exploring it simply.

神经网络模式识别及其实现，第10章 内含：ART 和VQ 算法。 终于上传完，本来想一次性发上来的，但结果失败了，只好一个一个发。-pattern recognition and neural network to achieve, Chapter 10 consisting of : ART algorithm and VQ. Finally upload End, originally wanted onto a one-time, but the result of the failure, but a one-fat.

JavaGenes is an evolutionary software system written in Java. It implements the genetic algorithm, simulated annealing, stochastic hill climbing and other search techniques.-JavaGenes is an evolutionary software system written in Java. It implements the genetic algorithm, simulated annealing, stochastic hill climbing and other search techniques.

《神经网络模式识别及其实现》源程序-"pattern recognition and neural network to achieve" source

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

tsp问题用神经网络方法解决-tsp uses neural network solution

模糊pid仿真，先运行模糊推理系统设计程序a.m，并调入内存，然后运行模糊控制程序b.m,对pid控制参数进行整定。-Pid fuzzy simulation, Fuzzy Inference System to run the design process am, and transferred to memory, and then run the procedure of fuzzy control bm, of pid control tuning parameters.