神经网络控制作为一种全新的智能控制方法,是解决非线性系统预测和控制问题的一种重要手段,受到了许多专家学者的广泛关注,是目前国内外研究的热点之一。本文着重研究前向...

自组织系统Kohonen网络模型。对于Kohonen神经网络，竞争是这样进行的：对于“赢”的那个神经元c，在其周围Nc的区域内神经元在不同程度上得到兴奋，而在Nc以外的神经元都被抑制。网络的学习过程就是网络的连接权根据训练样本进行自适应、自组织的过程，经过一定次数的训练以后，网络能够把拓扑意义下相似的输入样本映射到相近的输出节点上。网络能够实现从输入到输出的非线性降维映射结构：它是受视网膜皮层的生物功能的启发而提出的。~..~-Kohonen network model. For Kohonen neural network, competition is this : For the "winner" of neurons c, in its switching around the region neurons in varying degrees, to be excited, and the switching outside the neurons were inhibited. Network learning is a process in the network connecting the right under the training samples for adaptive, self-organizing process, after a certain number of training, network topology can sense similar to the mapping of the input samples similar to the output nodes. Network can be achieved from input to output of nonlinear reduced-dimensional mapping structure : it is subject to retinal cortex of the biological function inspired by. ~ ~ ..

　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

遗传算法的源程序例子，对于初学者有很大帮助，建议多-Genetic algorithm source code examples are very helpful for beginners, the proposed multi-

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

神经网络基本算法源程序，包含四个基本算法：BF,ART I,RBF,自组织算法，有详细的帮助文件。-basic neural network algorithm source, contains four basic arithmetic : BF, ART I, RBF, self-organizing algorithm, a detailed help files.

本人做的遗传算法源代码，在FORTRAN下打开即可以，可在WINDOUS环境下运行-I do genetic algorithm source code, under the FORTRAN that can be opened, in WINDOUS operating environment

这是模糊控制的核心原代码,应用于机电控制技术-fuzzy control

MILK 提供了一个实现和比较multi-instance学习算法的环境，基于weka机器学习系统（本站可以下载）-MILK provides a comparison and achieve multi-instance learning algorithm, based on machine learning system weka (site can be downloaded)

各种数值算法，包括：二叉树、贪心算法、搜索算法、遗传算法、模拟退火算法、分治策略等等。-A variety of numerical algorithms, including: binary tree, greedy algorithm, search algorithm, genetic algorithm, simulated annealing algorithm, sub-rule tactics and so on.

用遗传算法进行排序-genetic sequencing algorithms