用蚁群算法求解TSP（旅行商）问题，按照TSP.m中要求的格式输入程序参数即可运行！...

CMU的自主机器人的软件开发平台, 应用十分广泛, 基本上已经成为一种近似标准, 比较好用, 希望对开发机器人导航算法方面的人有用 -CMU autonomous robot software development platform for a wide range of applications, they have basically become a kind of similar to the standard, more useful, in the hope that the development of robot navigation algorithm useful side

用VC++实现的BP（反向传播负反馈）神经网络算法-with VC BP (BP negative feedback) neural network algorithm

这是一个使用VC++编写的“智能支持决策系统DSS源程序”，学习语言很有参考价值.分配问题/匈牙利算法-It is a preparation of the VC "intelligent decision support system DSS source," learn the language of great reference value. Distribution/Hungary algorithm

将遗传算法与蚁群算法相相结合，产生的一种新算法-genetic algorithm and ant algorithm combination of a new algorithm

中科院张的摄像机内外参数标定的源码和例子，可以在计算机视觉计算中试用-Chinese Academy of Sciences and outside the parameters of the camera calibration of the source code and examples, the computer can calculate the visual trial

本代码是人工智能中产生式的一个例子，功能是实现对有限动物特征识别的正向和反相推理。-the code is generated artificial intelligence-an example of that function is limited to the identification of the animal forward and inverse reasoning.

bp神经网络对短期电力负荷的预测，一周预测一天，输入是归一过的数据。-bp neural network for short-term load forecast, the forecast one day a week, enter the data have been normalized.

完成基于遗传算法的图像模式识别功能流程描述-To complete the image based on genetic algorithms pattern recognition function of the process described

vs2005开发 遗传算法求解144城市的旅行商问题-VS2005 to develop genetic algorithm 144 city traveling salesman problem

1。《遗传算法的数学基础》，张文修、梁怡编着 西安交通大学出版社 2000年第一版 遗传算法（genetic algorithm）是模拟自然界生物进化过程与机制求解问题的一类自组织与自适应的人工智能技术，已广泛应用于计算机科学、人工智能、信息技术及工程实践。 本书重点在于阐述遗传算法的数学基础。全书共分3章，第1章给出了遗传算法的几何理论，第2章给出了遗传算法的马尔可夫链分析，第3章给出了遗传算法的收敛理论。 本书可以作为应用数学、计算机科学、系统科学等专业研究生的教材，也可以作为研究遗传算法的参考书。 -1. "Genetic Algorithm mathematical basis," Zhang Xiu, Yi Liang compile Xi"an Jiaotong University Press in 2000 the first edition of the genetic algorithm (genetic algorit hm) is a simulation of natural biological evolution process and mechanisms to solve the problem of a kind of self-organization and adaptive artificial intelligence technology, has been widely used in computer science, artificial intelligence, information technology, and engineering practice. The book focused on the genetic algorithm described math. The book is divided into three chapters, one chapter is a genetic algorithm of the geometric theory Chapter 2 of the genetic algorithm is a Markov chain analysis, presented in Chapter 3 of the geneti