RSNNS-package(RSNNS)
RSNNS-package()所属R语言包：RSNNS
Getting started with the RSNNS package
入门的RSNNS包
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS
斯图加特神经网络的模拟器（SNNS）是一个库，包含了许多标准的神经网络实现。这个包包装SNNS
Details
详细信息----------Details----------
If you have problems using RSNNS, find a bug, or have suggestions, please contact the package maintainer by email, instead of writing to the general R lists or contacting the authors of the original SNNS software.
如果你有问题，使用RSNNS，发现了一个bug，或有任何建议，请联系通过电子邮件软件包的维护者，而不是写一般的R里面，列表或联系作者原SNNS软件。
If you use the package, please cite the following work in your publications:
如果您使用的包，请在出版物中引用了以下工作：
Bergmeir, C. and Ben铆tez, J.M. (2012), Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software, 46(7), 1-26. http://www.jstatsoft.org/v46/i07/
Bergmeir，C.和贝尼特斯，JM（2012年），用神经网络在斯图加特的神经网络仿真器：RSNNS。 [统计软件，46（7），1-26。 http://www.jstatsoft.org/v46/i07/~~V
The package has a hierarchical architecture with three levels:
包有一个分层架构的三个层次：
RSNNS high-level api (rsnns)
RSNNS高层次的API（rsnns）
RSNNS low-level api (SnnsR)
RSNNS低级别的API（SnnsR）
The api of our C++ port of SNNS (SnnsCLib)
的API，C + +，端口SNNS（SnnsCLib）
Many demos for using both low-level and high-level api of the package are available. To get a list of them, type:
许多演示同时使用低层次和高层次的API的包。为了得到它们的列表，键入：
library(RSNNS)
library(RSNNS)
demo()
demo()
It is a good idea to start with the demos of the high-level api (which is much more convenient to use). E.g., to access the iris classification demo type:
这是一个好主意，开始与高层次的API（这是更方便地使用）演示。例如，要访问的虹膜分类演示类型：
demo(iris)
demo(iris)
or for the laser regression demo type:
或激光回归演示类型：
demo(laser)
demo(laser)
As the high-level api is already quite powerful and flexible, you'll most probably normally end up using one of the functions: mlp, dlvq, rbf, rbfDDA, elman, jordan, som, art1, art2, artmap, or assoz, with some pre- and postprocessing. These S3 classes are all subclasses of rsnns.
作为高层次的API已经是相当强大的，灵活的，你很可能会结束了使用的功能之一：mlp，dlvq，rbf，rbfDDA elman，jordan，som，art1，art2，artmap或assoz，与一些预处理和后处理。这些S3类的所有子类rsnns。
You might also want to have a look at the original SNNS program and the SNNS User Manual 4.2, especially pp 67-87 for explications on all the parameters of the learning functions, and pp 145-215 for detailed (theoretical) explications of the methods and advice on their use.
您可能也想看看在原SNNS方案的SNNS用户手册4.2，特别是PP 67-87王希恩的学习功能，所有参数和pp 145-215详细（理论）王希恩的方法其使用和建议。
Demos ending with "SnnsR" show the use of the low-level api. If you want to do special things with neural networks that are currently not implemented in the high-level api, you can see in this demos how to do it. Many demos are present both as high-level and low-level versions.
演示结束与“SnnsR”的显示使用低级别的API。如果你想做些特别的事情的神经网络，目前尚未实现在高层次的API，你可以看到在演示曲怎么办呢。高级别和低级别的版本存在许多演示。
The low-level api consists mainly of the class SnnsR-class, which internally holds a pointer to a C++ object of the class SnnsCLib, i.e., an instance of the SNNS kernel. The class furthermore implements a calling mechanism for methods of the SnnsCLib object, so that they can be called conveniently using the "$"-operator. This calling mechanism also allows for transparent masking of methods or extending the kernel with new methods from within R. See $,SnnsR-method. R-functions that are added by RSNNS to the kernel are documented in this manual under topics beginning with SnnsRObject$. Documentation of the original SNNS kernel user interface functions can be found in the SNNS User Manual 4.2 pp 290-314. A call to, e.g., the SNNS kernel function krui_getNoOfUnits(...) can be done with SnnsRObject$getNoOfUnits(...). However, a few functions were excluded from the wrapping for various reasons. Fur more details and other known issues see the file /inst/doc/KnownIssues.
低级别的API主要由类SnnsR-class，它的内部保存一个指针，指向一个C + +对象的类SnnsCLib，即的SNNS内核的一个实例。此外，该类SnnsCLib对象的方法，实现了一个调用机制，使他们能够方便被称为使用“$”操作符。这种调用机制还允许透明屏蔽的方法，或以新的方式扩展内核内R. $,SnnsR-method。本手册中的内容开始SnnsRObject$R-函数添加到内核的RSNNS。的原始SNNS内核的用户界面功能的文档中可以找到SNNS用户手册4.2页290-314。的调用，例如，SNNS核心功能krui_getNoOfUnits(...)可以完成SnnsRObject$getNoOfUnits(...)。然而，从包装因各种原因被排除在外的一些功能。皮草更多的细节和其他已知问题，请参阅文件/安装/ DOC / KnownIssues。
Most of the example data included in SNNS is also present in this package, see snnsData.
大多数的示例数据在SNNS在此包中也存在，请参阅snnsData。
Additional information is also available at the project website:
有关其他信息，也可在项目网站：
http://sci2s.ugr.es/dicits/software/RSNNS
（作者）----------Author(s)----------
Christoph Bergmeir <a href="mailto:c.bergmeir@decsai.ugr.es">c.bergmeir@decsai.ugr.es</a>
and Jos茅 M. Ben铆tez <a href="mailto:j.m.benitez@decsai.ugr.es">j.m.benitez@decsai.ugr.es</a>
DiCITS Lab, Sci2s group, DECSAI, University of Granada.
<a href="http://dicits.ugr.es">http://dicits.ugr.es</a>, <a href="http://sci2s.ugr.es">http://sci2s.ugr.es</a>
参考文献----------References----------
http://www.jstatsoft.org/v46/i07/
http://www.ra.cs.uni-tuebingen.de/SNNS/
<h3>See Also</h3> <code>mlp</code>, <code>dlvq</code>, <code>rbf</code>, <code>rbfDDA</code>, <code>elman</code>,
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-28 22:08:44
