一种基于子波特征的模糊神经网络目标识别方法，有一定参考意义...

国内并行遗传算法文章大收集，研究并行遗传算法必备学习资料-Internal parallel genetic algorithm papers .It is essential learning material for donging research on Parallel genetic algorithm .

MATLAB的遗传算法工具-MATLAB genetic algorithm tool

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

曲线的平滑，应用贝塞尔曲线进行平滑，点击要平滑的点即可-With this program you can smooth your data automatically only by selecting the area to be smoothed with 2 clicks. Then INTERP1 is used to smooth. The operation is repeated until you press ENTER.

基于SVM，分本分类，，是关于两类分类的比较好的工具，国外网站提供，效果很好的-based SVM, the sub-classification is on the classification of the two types of relatively good tool, overseas sites provide good results, the

对于具有一定规模的TSP问题的求解，至今没有较完善的求解办法。本程序是用遗传算法来求解TSP问题。-for a certain size of the TSP solution and there has been no better way to solve. This procedure is used genetic algorithms to TSP.

实现了二维数据的K均值算法以及ISODATA分类算法-Achieve a two-dimensional data, as well as K-means algorithm ISODATA classification algorithm

神经网络C程序的第10章-neural network C procedures for the Chapter 10

一个关于BP网络的设计，实现一个函数的逼近，了解关于MATLAB神经网络工具箱的应用。-On the design of BP network, the realization of a function approaches, learn more about the MATLAB neural network toolbox applications.

2。《遗传算法――理论、应用与软件实现》，王小平、曹立明编着 西安交通大学出版社 2002年第一版 本书全面系统地介绍了遗传算法的基本理论，重点介绍了遗传算法的经典应用和国内外的新发展。全书共分11章。第1章概述了遗传算法的产生与发展、基本思想、基本操作以及应用情况；第2章介绍了基本遗传算法；第3章论述了遗传算法的数学基础；第4章分析了遗传算法的多种改进方法；第5章初步介绍了进货计算理论体系；第6章介绍了遗传算法应用于数值优化问题；第7章介绍了遗传算法应用于组合优化问题；第8章介绍了遗传算法应用于机器学习；第9章讨论了遗传算法在智能控制中的应用；第10章讨论了遗传算法与人工生命研究的相关问题；第11章介绍了遗传算法在图像处理、模式识别中的应用。-2. "Genetic Algorithm-- theory, application and software," Wang Xiaoping, Li-Ming Cao compile Xi"an Jiaotong University Press in 2002 the first book version of the comprehensive and systematic introduction of the genetic algorithm"s basic On focuses on the classical genetic algorithm use and the new development. The book is divided into 11 chapters. Chapter 1 provides an overview of the genetic algorithm for the selection and development of basic ideas, basic operation and the application; Chapter 2 introduces the basic genetic algorithms; Chapter 3 of the genetic basis of a mathematical algorithm; Chapter 4 Analysis of the genetic algorithm app