很好用的一个适用程序，由Visual C＋＋编程，有收藏价值

matlab 模拟退火算法simulated anealing 的工具箱-Matlab simulated annealing simulated anealing the Toolbox

一个基本遗传算法的C++语言实现，有三个文件构成。-A basic genetic algorithm C++ Language, there are three files.

专业介绍svm算法的书籍，适合有高等数学，线性代数，矩阵论，和数里统计基础的读者，他是一本全面介绍svm的书籍，从数学基础到应用，是svm学习者的一本好书。-Introduction SVM algorithm professional books, suitable for advanced mathematics, linear algebra,矩阵论, and several readers in the statistical basis, he is a comprehensive introduction to SVM books, from mathematical foundations to applications, is an SVM learner Good Books.

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.

神经网络的源代码，关于字符识别的一些东西-Neural network source code, something about character recognition

用MATLAB编写的svm支持向量机的分类,回归工具箱- MATLAB codes of support vector machine svm classification, regression Toolbox

greedy em 混和模型训练算法-greedy em hybrid model training algorithm

RBF算法matlab工具箱,里面有详细的说明文档.-RBF algorithm matlab toolbox, which has detailed documentation.

用matlab实现贝叶斯网络的学习、推理。-Bayesian network using matlab realize learning, reasoning.

同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-comparison with the genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications