Proposed based on minimizing the estimated generalization error bound of the non

　人工神经网络(Aartificial Neural Network，下简称ANN)是模拟生物神经元的结构而提出的一种信息处理方法。早在1943年，已由心理学家Warren S.Mcculloch和数学家Walth H.Pitts提出神经元数学模型，后被冷落了一段时间，80年代又迅猛兴起［1］。ANN之所以受到人们的普遍关注，是由于它具有本质的非线形特征、并行处理能力、强鲁棒性以及自组织自学习的能力。其中研究得最为成熟的是误差的反传模型算法(BP算法，Back Propagation)，它的网络结构及算法直观、简单，在工业领域中应用较多。-Artificial Neural Networks (Aartificial Neural Network, under the short title of ANN) is a simulation of the structure of biological neurons and the information put forward a method of treatment. At early 1943, by Warren S. Mcculloch psychologists and mathematicians Walth H. Pitts proposed mathematical model of neurons, was left out in the cold for some time, and the rapid rise of the 80" s [1]. ANN because the general concern of the people is the essence of it is because of the non-linear characteristics of parallel processing capabilities, strong robustness and self-organizing capacity of self-learning. Research one of the most sophisticated are the error-propagation model of the algorithm (BP algorithm, Back Pro

基于java实现的遗传算法的源代码，利于初学者了解GA以及如何用java实现。-Java-based genetic algorithm realize the source code, which will help beginners learn how to use GA as well as realize java.

打开压缩包，其中的word文档中提供了“m个工件分配给m架机床的效益最优化问题，使用一种遗传算法解决。”的整个编程思路和过程分析。望有相关需要的朋友喜欢-open compressed, the word document provided "m m allocated to the workpiece-machine efficiency optimization, the use of a genetic algorithm to solve." The entire programming ideas and process analysis. Expect relevant to the needs of friends like

用遗传算法优化神经网络权值的源程序-genetic algorithm optimization neural network value of the source

遗传算法基本原理PPT下载，适合初学者学习，一共有四部分。-The basic principles of genetic algorithms PPT download, suitable for beginners to learn, a total of four parts.

Fisher判别，用于模式识别的Fisher线性判别-Fisher Discriminant for pattern recognition Fisher Linear Discriminant

一个简单的遗传算法C++类-a simple genetic algorithm C

简单一元函数优化实例，利用遗传算法计算下面函数的最大值-One dollar a simple example of function optimization using genetic algorithm function below the maximum

神经网络模式识别及其实现，第四章。 内含：ALOPEX和BACKPROP程序。-pattern recognition and neural network to achieve, the fourth chapter. Intron : ALOPEX BACKPROP and procedures.

本书介绍了一些关于机器人的识别技术，值得学习和借鉴，作为基础资料不错-The book introduced a number of robot recognition technology, it is worth to learn from, as a good basis for information