一种支持向量机多专家决策算法，很有参考价值

本电子书很有意义！！！神经网络入门教程-The e-book makes sense! ! ! Neural Network Tutorial

基于RBF网络的机器人手臂运动关节的仿真实例。利用RBF网络实现机器人手臂运动关节的仿真实例建模。-RBF network based on the movement of the robot arm joint simulation. RBF network using the robot arm joint movement simulation modeling.

MATLAB版遗传算法解TSP问题-MATLAB version Genetic Algorithm for the Traveling Salesman Problem

关于人工神经网络的实用书籍，适合初学者，以及有一定基础的读者学习-Information on artificial neural networks and practical books, suitable for beginners, as well as readers of a certain basis for learning

遗传算法基本原理PPT下载，适合初学者学习，一共有四部分。-The basic principles of genetic algorithms PPT download, suitable for beginners to learn, a total of four parts.

机器人编程方法，主要是讲c的， 期刊论文-Robot programming method, mainly stresses c and Journal Papers

BP神经网络C＋＋实现-BP neural network implemented by C++

This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL

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算法，应用局部搜索法得到最优路径，已经经过优化。没有密码，可以使用，vc环境下使用。-TSP algorithm, using local search to be optimal path has been optimized. No password can be used, vc environments.