tsp问题用神经网络方法解决

利用Fisher分类器对数据进行分类，并画出决策面（含测试数据）-Fisher classifier using the data classification, and face painted in decision-making (including test data)

神经网络研究生收费模型的具体方案以及其应用-Neural network model of graduate student fees, as well as their application specific program

BP神经网络自适应步长训练算法，采用最小误差法，梯度下降法，自适应调节权值-BP neural network training anaysis, is realized by using error feed back, gradient descent applied updating of synaptic weights

使用Visual C++ 2003编写的遗传算法库，具有详细的说明文档，使用接口函数传入相关适应度函数与基因个数、迭代次数相关信息，进行目标优化，使用类模板的形式支持函数并行化-Using Visual C++ 2003 prepared by the genetic algorithm library, a detailed description of the document, use the interface function is passed the fitness function associated with the gene number, number of iterations relevant information to carry out objective optimization, the use of class template in the form of support function parallel

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

求解tsp问题的局部优化算法-the local optimization algorithm

蚁群算法及其在TSP中的应用。一篇很好的论文。-Ant colony algorithm and its application in the TSP. A very good paper.

遗传算法解决随机期望值模型问题的例题-random genetic algorithms to solve the problem expectations model example

神经网络蚁群算法，共三段，非常有用，快来下啊-Ant colony algorithm for neural networks, a total of three sections, very useful

Libsvm - MATLAB version-Libsvm- MATLAB version