neuralnet: Training of Neural Networks

作者：Frauke Günther, Stefan Gerd Fritsch · 发表于：The R Journal · 年份：2010 · DOI：10.32614/rj-2010-006 · 被引用次数：774 · 研究领域：Neural Networks and Applications、Spectroscopy and Chemometric Analyses、Face and Expression Recognition

Artificial neural networks are applied in many situations.neuralnet is built to train multi-layer perceptrons in the context of regression analyses, i.e. to approximate functional relationships between covariates and response variables.Thus, neural networks are used as extensions of generalized linear models.neuralnet is a very flexible package.The backpropagation algorithm and three versions of resilient backpropagation are implemented and it provides a custom-choice of activation and error function.An arbitrary number of covariates and response variables as well as of hidden layers can theoretically be included.The paper gives a brief introduction to multilayer perceptrons and resilient backpropagation and demonstrates the application of neuralnet using the data set infert, which is contained in the R distribution.