用BP神经网络逼近函数Z=SIN(X*Y),采用C++编程.

机器人编程方法，主要是讲c的， 期刊论文-Robot programming method, mainly stresses c and Journal Papers

单点交叉 单点变异 确定式选择法 自适应的遗传算法生成最小树-Adaptive genetic algorithm generates minimum spanning tree

java神经网络工具箱-neural network toolbox

goat遗传算法工具包，可参考使用，节省遗传算法开发时间-goat genetic algorithm tool kit, refer to the use of genetic algorithms to save development time

CPN算法的matlab实现，CPN人工神经网络-Matlab algorithm of network，The artificial neural network, network

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

LVQ(学习矢量量化)算法：它是Kohonen的有监督学习的扩展形式。融合了自组织和有导师监督的技术，学习方法是竞争的，但产生方式是有教师监督的，也就是说，竞争学习是在由训练输入指定的各类中局部进行。-LVQ (LVQ) algorithm : it is Kohonen of supervised learning the expansion of the form. The convergence of self-organization and supervision of the instructors, learning methods of competition, but there are ways teachers to supervise, meaning that the competitive learning is the training input from the designated categories of partial.

遗传算法在TSP中的应用的原代码， 具体的代码见内。-Genetic algorithm source code applies in TSP with detailed code

The software implements particle filtering and Rao Blackwellised particle filtering for conditionally Gaussian Models. The RB algorithm can be interpreted as an efficient stochastic mixture of Kalman filters. The software also includes efficient state-of-the-art resampling routines. These are generic and suitable for any application.-The software implements particle filteri Vi and Rao Blackwellised particle filtering az r conditionally Gaussian Models. The RB algori thm can be interpreted as an efficient stochast ic mixture of Kalman filters. The software also includes efficient state-of-the-art resampl ing routines. These are generic and suitable az r any application.

本程序实现的功能为建立一分类器,用于进行有监督的分类学习.-the program for the establishment of a functional classification, which is used for supervised classification study.