Eigenface和Fisherface表现在姿势

遗传算法实用程序5（pascal)-GA Utility 5 (pascal)

贝叶斯算法是基于贝叶斯定理 P(H|X) = P(X|H)P(H) / P(X).。对于多属性的数据集，计算 P(X|Ci) 的开销非常大，为减低计算复杂度，我们做条件独立的假设，即给定元组的类标号，假定属性值有条件地相互独立，即在属性间不存在依赖关系。此程序仅为算法的一个实现，根据训练数据训练分类器-Bayesian algorithm is based on the Bayes theorem P (H | X) = P (X | H) P (H)/P (X).. For multi-attribute data sets, computing P (X | Ci) of the overhead is very large, in order to reduce the computational complexity, we do conditional independence assumption that a given tuple class label, it is assumed that property values conditionally independent of each other, that does not exist in the inter-attribute dependencies. This procedure is only an implementation of algorithm, according to training data classifier training

这是用prolog写的几个程序：程序１是一个旅行社关系系统，程序２是一个家族关系系统，程序３是一个成绩管理系统-This is prolog written several programs : a procedure is a travel agency relations systems, procedures 2 is a family relationship systems, procedures 3 is a performance management system

进化策略进行函数寻优Evolutionary function optimization strategy-Evolutionary function optimization strategy

研究生课程机器人控制基础大作业 矩形目标检测-DetectRectangleObjuct

BP智能岩土工程，为初学者提供学习。高手可以进行更改程序不能处理大数据量的错误。-BP smart geotechnical engineering, to beginners learning. Experts can not change procedures to deal with the large volume of data errors.

针对热电厂锅炉补水水处理控制程序,用simens s224,包括多介质,反渗透,混床控制 -Water treatment for power plant boiler control procedures, and simens s224, including multi-media, reverse osmosis, mixed bed control

BP神经网络的基本算法. 基于对话框-BP neural network algorithm. Based on the dialog box

本人编写的incremental 随机神经元网络算法，该算法最大的特点是可以保证approximation特性，而且速度快效果不错，可以作为学术上的比较和分析。目前只适合benchmark的regression问题。 具体效果可参考 G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006. -I prepared by incremental random neural network algorithm, which is characterized by the largest approximation properties can be guaranteed, and fast good results can be used as an academic comparison and analysis. The current benchmark is only suitable for the regression problem. Specific effects may refer G.-B. Huang, L. Chen and C.-K. Siew,