资源列表
Map-of-China-coloring-problem
- 基于遗传算法的中国地图着色问题，额外给出省份名称与相邻关系的txt文件和中国地图的jpg图片文件-Map coloring problem based on genetic algorithms, province name is given txt file with neighboring relations, and China map jpg picture file
1010280956fe8fe2fe0827f49e
- 基于混沌时间序列的神经网络预测，包括一步和多步预测算法-Based on neural network prediction of chaotic time series, including step and multi-step prediction algorithm
Genetic Nonlinear Matlab
- 遗传算法虽然全局搜索能力较强，但是局部搜索能力较弱，一般只能搜索到函数优化问题的次优解，而不是最优解，特别是函数具有多个峰值时，遗传算法易陷入局部极小，不能找到真正的全局最优解。非线性规划因多采用梯度下降方法求解，而具有极强的局部搜索能力。因此，本源代码结合两种算法的优点，一方面采用遗传算法进行全局搜索，另一方面采用非线性规划进行局部搜索，以得到函数优化问题的全局最优解。实验证明，这种方法不仅能解决多峰函数寻优易陷入局部极小的问题，而且具有很高的迭代寻优效率，取得了满意的结果。-Global s
decision-tree
- 基于决策树的增量学习算法相关文档，主要用于文本分类-Incremental learning algorithm based on decision tree document, mainly for text classification
PCA-PAPER
- PCA的相关文献，很不错，可以看看。找了好久，这个讲的清晰。-PCA，code，download this paper ,you can clear the PCA.
pso2012-04-03
- 粒子群算法的matlab实现，包含主文件pso.m和例子程序psodemo.m和psodemo2.m等，每个文件都有详细的注释说明。-Particle Swarm algorithm matlab realize, contains the the master file pso.m and example program psodemo.m and psodemo2.m etc., each file has detailed notes.
Kmeans
- 划分聚类中的K-means算法，运用的欧式距离作为度量距离-The instance of K-means clustering algorithm
ultimateliu
- 先锋3机器人的避障、沿墙走等综合实验程序 比较简单 可通过mobilesim进行仿真 -Pioneer 3 robot obstacle avoidance, wall to walk comprehensive experimental procedure is relatively simple simulation through mobilesim
Robot-Dynamics-and-Control
- 国外关于机器人动力学及其控制的经典书籍，值得学习。-Abroad classic books on the robot dynamics and control, it is worth learning.
uspsFisher
- 用Fisher算法对usps数据集进行分类，对每个部分都有很好的说明，对画图也有详细说明-USPS dataset using Fisher algorithm classification, each part has a good descr iption of the drawing there are described in detail
MWC
- MWC飞控详细程序1.0-1.8，详细具体的算法大全及分析，为研究飞控代码者提供一个阶梯。-Detailed program MWC MWC flight control flight control procedures, detailed algorithm Daquan, provide a ladder for the study of flight control.
DecisionTree
- ID3 人工智能，机器学习算法，自动生成决策树-ID3 artificial intelligence, machine learning algorithms, automatically generate decision trees