这个工具箱包含了重新

一个神经网络小脑CMACmatlab算法例子- A nerve network cerebellum CMACmatlab algorithm example

This code in this directory implements the binary hopfield network.Source code may be found in HOPNET.CPP. A sample training file is H7x8N4.trn. Sample test pattern files are: H7x8D4.TST, H5x8D7.TST,H5x8D7.TST and H5x8D9.TST, Output of the program goes to both the screen and a file, ARCHIVE.LST.

面试老鼠和猫连动问题的集中解决方案 可以建议应付面试问题-interview rats and cats even move the concentrated solution can meet the proposed interview questions

bp神经网络计算的源程序-bp neural network calculation of the source

神经网络模式识别及其实现，第四章。 内含：ALOPEX和BACKPROP程序。-pattern recognition and neural network to achieve, the fourth chapter. Intron : ALOPEX BACKPROP and procedures.

数据挖掘中dbscan算法的vc实现的源代码-Source code of DBSCAN algorathm of data mining with VC

兔子狐狸生态平衡模拟器，可以模拟兔子狐狸相互依存的状况。-The zoology balance simulator of rabbit and fox ,which can simulate the status of rabbit and fox depended on each other.

用模拟退火法高效的计算图论中哈密顿贿赂的最短路径-simulated annealing method of calculating efficient graph theory Hamiltonian bribery Shortest Path

MATLAb语言的遗传算法工具箱，比较全。-MATLAb language of genetic algorithm toolbox, the entire comparison.

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