RSNNS
RSNNS: Neural Networks in R using the Stuttgart Neural Network Simulator (SNNS). The Stuttgart Neural Network Simulator (SNNS) is a library containing many standard implementations of neural networks. This package wraps the SNNS functionality to make it available from within R. Using the RSNNS low-level interface, all of the algorithmic functionality and flexibility of SNNS can be accessed. Furthermore, the package contains a convenient high-level interface, so that the most common neural network topologies and learning algorithms integrate seamlessly into R.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
Sorted by year (- Vasilis Nikolaidis: The nnlib2 library and nnlib2Rcpp R package for implementing neural networks (2021) not zbMATH
- Barinder Thind, Sidi Wu, Richard Groenewald, Jiguo Cao: FuncNN: An R Package to Fit Deep Neural Networks Using Generalized Input Spaces (2020) arXiv
- Ron Wehrens; Johannes Kruisselbrink: Flexible Self-Organizing Maps in kohonen 3.0 (2018) not zbMATH
- Lala Riza; Christoph Bergmeir; Francisco Herrera; José Benítez: frbs: Fuzzy Rule-Based Systems for Classification and Regression in R (2015) not zbMATH