资源列表
PGA.rar
- 简单并行遗传算法代码，由串行遗传算法改编而来。,Simple parallel genetic algorithm code, adapted from from the serial genetic algorithm.
pca-kpca.zip
- 此为pca ,kpca的源代码，智能技术课上用的,This is a pca, kpca source code
C50
- 功能强大的决策树分类算法，是C4.5的改进版本，但在精度，速度和内存开销上均有了很大的改进。目前由rulequest公司管理，其可执行程序版本为商业版本，此GPL许可的源代码对外发布。-Both C4.5 and C5.0 can produce classifiers expressed either as decision trees or rulesets. In many applications, rulesets are preferred because they are simp
BP
- 关于旅游期间的客流量预测，用的是BP神经网络MATLAB编程-During the traffic forecasts on tourism, using a BP neural network MATLAB Programming
BPnet
- 应用MATLAB编写BP神经网络的程序，实现大气质量的预测，训练效果好，是很好的BP网络参考的例子-Application of BP Neural Network MATLAB program written to achieve air quality forecasting, training effect, is a good example of BP network reference
GA-based-on-the-graph-edge
- 基于遗传算法的有向图的边序列遍历算法Matlab源码.此函数实现遗传算法，用于用于有向图的边遍历序列的优化 GreenSim团队原创作品-Genetic algorithms are based on the edge to the graph traversal algorithm sequence of Matlab source code. This function implements the genetic algorithm used for edge directed graph
ACMRAS
- 基于MARS的PMSM无速度传感器矢量控制系统研究-MARS-based speed sensorless PMSM vector control system
Thesis-On-Cognitive-Radio
- This thesis aims to clearly describe the cognitive radio and its components and operations. Moreover, it aims on describing the expected outcome from the most common techniques that are proposed for use in cognitive radios. In addition, it describes
test3_5
- 解方程组的雅克比迭代法，其中描述了迭代法的收敛条件-Jacobi equations solution iteration, which describes the convergence conditions for iterative methods
Fortran_bp
- BP（Back Propagation）网络是1986年由Rumelhart和McCelland为首的科学家小组提出，是一种按误差逆传播算法训练的多层前馈网络，是目前应用最广泛的神经网络模型之一。BP网络能学习和存贮大量的输入-输出模式映射关系，而无需事前揭示描述这种映射关系的数学方程。它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。BP神经网络模型拓扑结构包括输入层（input）、隐层(hide layer)和输出层(output layer)。-
osgartsimple
- osgart的多标志物显示 实现多个标志物上注册多个虚拟物体-osgart multi-marker shows up multiple markers on multiple virtual objects
Data-Mining-PPT
- 这是一个数据挖掘PPT的详细介绍,包括分析预测，聚类分析，挖掘频繁模式、关联和相关等-PPT is a detailed descr iption of data mining, including the analysis and forecasting, cluster analysis, mining frequent patterns, association and correlation