用局部最优探索算法求解tsp问题。。。matlab编写。附有城市坐标用于检验。...

这是模糊控制的核心原代码,应用于机电控制技术-fuzzy control

在vc上运行的回答集求解器源代码，能够供在人工智能上学习的学习者使用-Running in vc answer set solver source code, for use in artificial intelligence to learn the use of the learners

另一个遗传算法程序-Another genetic algorithm program

扩展的Infomax算法，具有比较快的收敛速度！而且程序非常简洁,明了-expansion of Infomax algorithm, with a faster rate of convergence! The procedure is very simple, straightforward

基于神经网络的乒乓球机器人乒乓球轨迹预测代码，神经网络的参数可能有待进一步改进-code of PingPong Robot trajectory predicting based on neural network

matlab小程序，为初学者提供一些常用的例子-There provide some basic matlab programs that will help a biginer a lot.

这是一个模式识别中的最近邻分类算法的一个简单仿真分类实例，其中female.txt和male.txt是训练样本，test.txt是测试样本，分类效果非常好，对于模式学习的初学者将会有很大帮助。-This is a pattern recognition algorithm for nearest neighbor classification simulation of a simple classification example, is one of female.txt and male.txt training samples, test.txt is the measurement, classification effect is very good for beginners will be learning model there is a great help.

用禁忌搜索解决TSP问题，程序中使用对路径长度禁忌的方法，运行效率比较高。-Tabu search solution TSP, procedures for the use of the taboo path length, operating more efficient.

基于遗传算法的体育彩票拟合与预测，经检验，中奖率高于随机号码。-based on genetic algorithm fitting sports lottery and forecasts, on examination, the winning rate than random numbers.

6。《非数值并行算法――遗传算法》，刘勇等着 科学出版社 1995年第一版 本书系统地叙述了非数值并行算法之一的模拟退火算法的基本原理以及最新进展，同时为了便于读者解决实际问题，书中对具体算法的步骤作了详细介绍。本书共分七章，第一章介绍算法的思想、特点。发展过程和前景。第二章介绍算法的基本理论。第三章讨论算法解连续优化问题。第四章利用算法设计和优化神经网络。第五章介绍在组合优化中的应用。第六章介绍应用遗传程序设计解决程序设计自动化问题。第七章对遗传算法和其它适应性算法进行比较。 本书可供高校有关专业的师生、科研人员、工程技术人员阅读参考。-6. "Non- numerical parallel algorithms-- Genetic Algorithm" Liu Yong waiting 520-531 1995 version of the first book to systematically describe the non- numerical parallel algorithms one count of simulated annealing Law and the basic tenets of the latest progress, in order to help readers solve practical problems, book of the specific algorithm steps in detail. The book is divided into seven chapters, the first chapter describes the idea algorithm, characteristics. The development process and prospects. The second chapter describes the basic algorithm theory. The third chapter discusses Algorithm for continuous optimization problems. Chapter IV using the algorithm design and optimization of neural netw