资源列表
PageRank
- 自己实现的pagerank算法，比较经典的一个的、算法，有兴趣的可以看看。-Realize their own pagerank algorithm, more classic one, algorithms, are interested can look at.
GAPNN
- 用遗传算法优化BP神经网络的权值和误差，自己写的代码，可以运行-Using genetic algorithm to optimize BP neural network weights and error, write your own code, you can run
K-means-clustering
- 关于K-means聚类的算法描述，原创，包含有大量训练和测试数据-About K-means clustering algorithm descr iption, originality, contains a large number of training and test data
Intelligent-Optimization
- 基于蚁群算法进行路径规划，原创，可以自己设置路径测试-Ant colony algorithm for path planning, original, you can set up their own path testing
gatsp
- 用遗传算法解决Tsp问题，这是一个老问题了，自己写的程序，按照自己的思路来的，效果还行，GUI界面-Tsp using genetic algorithms to solve the problem, this is an old question, write their own programs, according to their own ideas, and the results were OK, GUI interface
MELP2.4K
- MELP2.4K代码,可运行的语音压缩代码 -MELP2.4K CODE
knnClassification
- 十大经典人工智能算法之一——K最近邻Matlab实现-One of the top ten classical artificial intelligence algorithms- K nearest neighbor Matlab implementation
bp
- 鸢尾花数据分类 采用bp（神经网络前向反馈）算法 -The iris data classification using the algorithm of bp （neural network forward feedback)
BP
- 使用VC++编写。BP人工神经网络计算程序，里面自带模拟数据。-Use VC++ writing. BP artificial neural network calculation program, which comes with analog data.
collision-detection-algorithm-
- 虚拟环境中碰撞检测基本原理及算法知识,是国内一位比较早研究该算法的学者，一共四篇，具有连续性，帮助大家对碰撞检测算法从初级到入门。欢迎大家下载交流。-Knowledge of basic principle and algorithm of collision detection in virtual environment, the algorithm is a comparative study of scholars, a total of four papers, continuity,
Particle-swarm-algorithm-
- 粒子群算法的基本介绍的word文档，该文件和普通的文字介绍不同，它是通过一个图形例子，很形象地描述粒子群算法的内涵。文件后有代码。非常适合初学者，帮助快速理解-The basic particle swarm algorithm is introduced, and the file and ordinary text introduces different, it is through a graphic example, very vividly describes the connota
graduation-design-project-paper-s
- 粒子群优化(优化)毕业设计项目报告(截图_数据) _(包括源代码,实验数据非常全面的)-Particle swarm optimization (optimization) graduation design project paper screenshots _ _ (including source code, experimental data very comprehensive)