这是通过A*算法实现8数码问题，求解可能出现的目标状态。通过回溯的方法实现对A*算法。不同于其它状态图的搜索实现8数码问题。...

模拟遗传算法，JAVA-Simulation of Genetic Algorithms, JAVA

中科院的《高级人工智能》课件，希望对大家学习有帮助。-Chinese Academy of Sciences

本代码用基于steve语言的breve软件编写的，此软件是一种3D人工智能的环境模拟器，可以用来构建AGENT-BASED SYSTEM和模拟机器人行为。本代码实现功能如下：机器人服务模拟。自动取得饮料，递送给客人，客人喝完后，另一种清理机器人处理瓶子并运送垃圾堆。主要为了证明机器人运动中的一种Deliberative设计结构。软件下载地址：http://www.spiderland.org/breve/download.php 打开我上传的文件后，点运行就可以看效果。-The code-based software steve language breve prepared, the artificial intelligence software is a 3D environment simulator can be used to build the AGENT-BASED SYSTEM and robot behavior simulation. Realize this function code is as follows: service robot simulation. Automatically beverages, delivered to the guests, the guests after the finish, another robot cleaning up the bottle and the delivery of garbage treatment. Mainly in order to prove that the movement of a robot design structure Deliberative. Software Download: http://www.spiderland.org/breve/download.php open my uploaded file, you can look at the effect of running.

研究生课程机器人控制基础大作业 检测直线与曲线-Robot Control Technology-DetectStraightlineAndCurves use matlab

一遗传算法的例子源程序-Example of a genetic algorithm source code

C均值法，用于模式识别中的聚类算法。非常好用！-C- Means method for pattern recognition algorithm for clustering. Very handy!

matlab实现机器人基于两点法的路径规划，路径规划的实用例程-Matlab 2:00 achieve robot path planning method, path planning practical routines

Simon Haykin的 《Neural NetWorks》例子原码，相当经典。相信很有用，特别SVM PCA等-Simon Haykin "Neural NetWorks" examples of the original code, very classic. I believe very useful, especially in such SVM PCA

该文档是清华大学的模式识别教程，该版本是网页版本，浏览非常方便，内容也非常详细，非常适合初学者，个人举得特别是支持向量机那一章描述很棒！-this document is from Tsinghua University,this is the html version,it is convenient to read, it is very suitable for the elementary readers,in my own opinion,the charter of svm is very detail and easily understandable.

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