art1(RSNNS)
art1()所属R语言包：RSNNS
Create and train an art1 network
建立和培养一个ART1网络
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Adaptive resonance theory (ART) networks perform clustering by finding prototypes. They are mainly designed to solve the stability/plasticity dilemma (which is one of the central problems in neural networks) in the following way: new input patterns may generate new prototypes (plasticity), but patterns already present in the net (represented by their prototypes) are only altered by similar new patterns, not by others (stability). ART1 is for binary inputs only,
自适应共振理论（ART）网络进行聚类，找到原型。它们的主要目的，解决稳定性/可塑性困境（这是在神经网络中的核心问题之一），在以下的方式：新的输入模式，可能会产生新的原型（塑性），但模式中已经存在的净（由他们的代表原型）只改变了类似的新的模式，而不是由别人（稳定性）。 ART1是唯一的二进制输入，
用法----------Usage----------
## Default S3 method:[默认方法]
art1(x, dimX, dimY, f2Units=nrow(x), maxit=100, initFunc="ART1_Weights",
initFuncParams=c(1, 1), learnFunc="ART1", learnFuncParams=c(0.9, 0,
0), updateFunc="ART1_Stable", updateFuncParams=c(0),
shufflePatterns=TRUE, ...)
参数----------Arguments----------
参数：x
a matrix with training inputs for the network
矩阵的网络培训投入
参数：dimX
x dimension of inputs and outputs
x维度输入和输出
参数：dimY
y dimension of inputs and outputs
y维度输入和输出
参数：f2Units
controls the number of clusters assumed to be present
控制聚类数，假设存在
参数：maxit
maximum of iterations to learn
最大的迭代学习
参数：initFunc
the initialization function to use
使用初始化函数
参数：initFuncParams
the parameters for the initialization function
初始化函数的参数
参数：learnFunc
the learning function to use
学习功能使用
参数：learnFuncParams
the parameters for the learning function
学习功能的参数
参数：updateFunc
the update function to use
使用更新功能
参数：updateFuncParams
the parameters for the update function
更新功能的参数
参数：shufflePatterns
should the patterns be shuffled?
应的模式被打乱？
参数：...
additional function parameters (currently not used)
附加功能参数（目前没有使用）
Details
详细信息----------Details----------
art1: Learning in an ART network works as follows: A new input is intended to be classified according to the prototypes already present in the net. The similarity between the input and all prototypes is calculated. The most similar prototype is the winner. If the similarity between the input and the winner is high enough (defined by a vigilance parameter), the winner is adapted to make it more similar to the input. If similarity is not high enough, a new prototype is created. So, at most the winner is adapted, all other prototypes remain unchanged.
art1：学习中的ART网络的工作原理如下：一个新的输入进行分类的目的是根据已经存在的净原型。所有的输入和原型之间的相似性的计算方法。最相似的原型是赢家。如果是足够高的（定义由警惕性参数）之间的相似性的输入和赢家，赢家适于，使其更类似于输入。如果相似性不够高，创建一个新的原型。因此，最适用的赢家，所有其他的原型保持不变。
The architecture of an ART network is the following: ART is based on the more general concept of competitive learning. The networks have two fully connected layers (in both directions), the input/comparison layer and the recognition layer. They propagate activation back and forth (resonance). The units in the recognition layer have lateral inhibition, so that they show a winner-takes-all behaviour, i.e., the unit that has the highest activation inhibits activation of other units, so that after a few cycles its activation will converge to one, whereas the other units activations converge to zero. ART stabilizes this general learning mechanism by the presence of some special units. For details refer to the referenced literature.
ART网络的体系结构是下列：ART的基于竞争学习的更一般的概念，。网络有两个完全连接的层（在两个方向上），输入/比较层和识别层。他们传播激活来回（共振）。在识别层的单位有侧抑制，使他们表现出赢家通吃所有的行为，即具有最高的激活抑制活化的其他单位的单位，这样几个周期后，其激活将收敛到一个，而其他单元激活收敛到零。 ART稳定这个一般的学习机制存在一些特殊的单位。有关详细信息，请参阅参考文献。
The default initialization function, ART1_Weights, is the only one suitable for ART1 networks. It has two parameters, which are explained in the SNNS User Manual pp.189. A default of 1.0 for both is usually fine. The only learning function suitable for ART1 is ART1. Update functions are ART1_Stable and ART1_Synchronous. The difference between the two is that the first one updates until the network is in a stable state, and the latter one only performs one update step. Both the learning function and the update functions have one parameter, the vigilance parameter.
默认的初始化函数，ART1_Weights，是唯一一个适合ART1网络。它有两个参数，这是在SNNS用户手册pp.189解释。通常是罚款为默认设置为1.0。唯一的学习功能，适用于ART1是：ART1。更新功能是ART1_Stable和ART1_Synchronous。两者之间的区别是，在第一个更新，直到网络是在一个稳定的状态，和后者的1只执行一个更新步骤。学习功能和更新功能有一个参数，警戒参数。
In its current implementation, the network has two-dimensional input. The matrix x contains all (one dimensional) input patterns. Internally, every one of these patterns is converted to a two-dimensional pattern using parameters dimX and dimY. The parameter f2Units controls the number of units in the recognition layer, and therewith the maximal amount of clusters that are assumed to be present in the input patterns.
在其当前实现中，该网络具有两维输入。的矩阵x包含所有输入模式（一维）。在内部，这些模式中的每一个的是，转换使用参数dimX和dimY的一个两维图案。参数f2Units控制中的识别层的单位数，以及与其最大的量的簇，被假定为在输入模式中的本。
A detailed description of the theory and the parameters is available from the SNNS documentation and the other referenced literature.
从SNNS文档和其他参考文献的理论和参数的详细说明。
值----------Value----------
art1.default: an rsnns object. The fitted.values member of the object contains a list of two-dimensional activation patterns.
art1.default：rsnns对象。 fitted.values成员对象包含了一系列的二维激活模式。
参考文献----------References----------
http://www.ra.cs.uni-tuebingen.de/SNNS/
<h3>See Also</h3>
实例----------Examples----------
## Not run: demo(art1_lettersSnnsR)[＃不运行：演示（art1_lettersSnnsR）]
data(snnsData)
patterns <- snnsData$art1_letters.pat
inputMaps <- matrixToActMapList(patterns, nrow=7)
par(mfrow=c(3,3))
for (i in 1:9) plotActMap(inputMaps[[i]])
model <- art1(patterns, dimX=7, dimY=5)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-28 22:04:33
