模拟退火c++的算法程序，广泛应用于最优化、运筹学、人工智能、遗传算法等领域，具有很好的学习价值...

统计学习理论是建立在一套较坚实的理论基础之上的，为解决有限样本学习问题提供了 一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比 如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法： 支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当 今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified framework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.

基于梯度法编写的RBF神经网络程序，实现对输入数据的逼近-Gradient method based on the preparation process of the RBF neural network to achieve the approximation of the input data

主要实现了烧结矿碱度的神经网络预测，训练精度高-The main achievement of the sinter basicity of the neural network prediction, training and high precision

//主函数，主要是用于启动用于模拟虚存活动和进行监控的两个线程；-//main function is mainly used to launch virtual memory used to simulate and monitor activities of the two threads;

用c++实现的决策树算法，windows环境下，希望对学习数据结构和算法的朋友有所帮助。-achieve with the Decision Tree Algorithm, windows environment, and I hope to learn from data structures and algorithms friends help.

本程序利用蚂蚁算法解决TSP（旅行商问题）问题-This procedure using ant algorithm to solve TSP (Traveling Salesman Problem) problem

这个软件可以对输入的象素矩阵进行识别。是根据《Neural Network Design》（Martin T. Hagan等）中的关于有监督的Heb学习编写的。使用JBuilder开发的界面，使用JCreator开发的核心。-the software can input pixel matrix identification. Based on "Neural Network Design" (Martin T. Hagan, etc.) on the supervision of a study prepared by the Heb. Using JBuilder development of the interface, use JCreator development of the core.

文中设计了一个3层径向基神经网络(RBFN)用于对企业的5项评价指标进行聚类分析，并与蚁群算法做了比较分析。RBFN由输入层 到隐含层采用传统的K一均值算法，隐含层到输出层通过“模2递减”学习速率的BP学习；蚁群算法根据信息素的分配能够自动调整收索 路径，从而达到数据自动聚类的目的。结果表明，与蚁群算法相比，改进RBFN具有快速收敛、自动识别奇异样本的优点，而蚁群算法 无须教师学习，并能够达到全局最优。-In this paper, we designed a 3-layer RBF neural network (RBFN) for the 5-to-business evaluation indicators cluster analysis and ant colony algorithm has done a comparative analysis. RBFN from input layer to hidden layer using the traditional K-means algorithm, hidden layer to output layer through the Mode 2 decreasing learning rate of BP learning ant colony algorithm based on pheromone can automatically adjust the allocation of land Faso path, thereby to achieve the purpose of automatic data clustering. The results showed that compared with the ant colony algorithm to improve the RBFN has a fast convergence, automatic identification of singular advantage of the sample, while the ant colony algorithm do not need teachers to learn and be able to reach the global optimum.

粒子群算法，这是一个标准粒子群优化算法源码-PSO

使用最小二乘支持向量机对多维pyrim数据进行回归，需要下载最小二乘支持向量机工具箱。-use of least squares support vector machines for multidimensional data pyrim return need to download least squares support vector machines toolbox.