清华大学不确定规划教材中的用遗传算法训练神经元网络的混合智能算法2...

BP神经网络C语言程序包-BP neural network C language package

遗传算法软件包，其中有例子配合理解，十分不错-GA package, which is an example with understanding, very good

一个神经网络原型代码，针对的例子为《Perceptron Learning》 (Russell & Norvig， 第742页)-A neural network prototype code, for example for

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

模糊K近邻分类器，模式识别和机器学习经常要使用的一个分类器-K neighbor fuzzy classifier, pattern recognition and machine learning are often used in a classifier

在c语言中实现的多目标优化，同时能够上使用者学习研究带来很大的方便-C 实 亩目呕 同 时 芄 学魏芯 艽 姆

介绍了matlab常见问题，用于人工神经网络的BP算法-Frequently Asked Questions matlab introduced for the artificial neural network BP algorithm

svm Main_SVM_One_Class 用于svm分类的 在matlab中运用 -svm classification svm Main_SVM_One_Class for use in matlab

一个遗传算法例子-example of a genetic algorithm

统计学习理论是建立在一套较坚实的理论基础之上的，为解决有限样本学习问题提供了 一个统一的框架。它能将很多现有方法纳入其中，有望帮助解决许多原来难以解决的问题（比 如神经网络结构及参数选择问题）；同时。在这一理论基础上发展了一种新的通用学习方法： 支持向量机（Support Vector Machine，SVM）。它己表现出很多优秀的性能，并已经成为当 今机器学习领域的研究热点。-Statistical learning theory is based on a more solid theoretical basis, in order to solve the learning problems of limited samples provides a unified framework. It will be incorporated into many existing methods, which could help solve many of the original difficult to resolve problems (such as neural network structure and parameters selection) the same time. Developed on the basis of this theory, a new universal learning methods: support vector machine (Support Vector Machine, SVM). It has been shown a lot of excellent performance and has become the hotspot of machine learning research in the field.