Scalable k

这是一个nsga的应用问题。算法可能不是最优的。-application of nsga

本电子书很有意义！！！神经网络入门教程-The e-book makes sense! ! ! Neural Network Tutorial

神经网络学习二维非线性函数 ,多维非线性辨识的MATLAB程序-neural network learning 2D nonlinear function, multi-dimensional nonlinear identification procedures MATLAB

人工智能是现在科学发展的热门，该程序通过人工神经网络的原理实现图像的分类功能-The scientific development of artificial intelligence is now the most popular, the program adopted the principle of artificial neural network classification of image features

An Introduction to Genetic Algorithms for Scientists and Engineers By David Coley, World Scientific Press, 1999 (US$28 / £ 19.04) 书中的算法源程序-An Introduction to Genetic Algorithms for Scientists and Engineers By David Coley, World Scientific Press, 1999 (U.S. $ 28 /

BP分类器，用与识别不同模式。-BP classifier, and used to identify different models.

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

程序代码说明 神经网络的实例,内附详细说明-Code description examples of neural network, containing a detailed description of

遗传算法GA的源代码及例子-genetic algorithm source code and examples

Locally weighted polynomial regression LWPR is a popular instance based al gorithm for learning continuous non linear mappings For more than two or three in puts and for more than a few thousand dat apoints the computational expense of pre dictions is daunting We discuss drawbacks with previous approaches to dealing with this problem