R: A collection of Neural Networks and tools to create custom...

nnlib2Rcpp-package {nnlib2Rcpp}

R Documentation

A collection of Neural Networks and tools to create custom models

Description

This package provides a module (NN module) to define and control neural networks containing predefined or custom components (layers, sets of connnections etc.). These components may have been derived from nnlib2 NN components (written in C++) or defined using R.

It also contains a small collection of ready-to-use Neural Networks (NN), i.e. versions of Autoencoder, Back-Propagation, Learning Vector Quantization and Matrix Associative Memory NN. More information and examples for each of the above can be found in its documentation (see below).

Ready-to-use Neural Networks:

Plain Back-Propagation (BP-supervised) (BP)
Learning Vector Quantization (LVQ-supervised) (LVQs)
Learning Vector Quantization (LVQ-unsupervised) (LVQu)
Matrix Associative Memory (MAM-supervised) (MAM)
Autoencoder (unsupervised) (Autoencoder)

Custom Neural Networks:

NN module (NN)

Author(s)

Author/Maintainer:

Vasilis Nikolaidis vnnikolaidis@gmail.com

Contributors:

Arfon Smith [contributor]
Dirk Eddelbuettel [contributor]

References

Nikolaidis, V. N., (2021). The nnlib2 library and nnlib2Rcpp R package for implementing neural networks. Journal of Open Source Software, 6(61), 2876, doi:10.21105/joss.02876.

References for the ready-to-use NN models (can also be found in related documentation):

Kohonen, T (1988). Self-Organization and Associative Memory, Springer-Verlag.; Simpson, P. K. (1991). Artificial neural systems: Foundations, paradigms, applications, and implementations. New York: Pergamon Press.
Pao Y (1989). Adaptive Pattern Recognition and Neural Networks. Reading, MA (US); Addison-Wesley Publishing Co., Inc.
Simpson, P. K. (1991). Artificial neural systems: Foundations, paradigms, applications, and implementations. New York: Pergamon Press.
Philippidis, TP & Nikolaidis, VN & Kolaxis, JG. (1999). Unsupervised pattern recognition techniques for the prediction of composite failure. Journal of acoustic emission. 17. 69-81.
Nikolaidis V.N., Makris I.A, Stavroyiannis S, "ANS-based preprocessing of company performance indicators." Global Business and Economics Review 15.1 (2013): 49-58, doi:10.1504/GBER.2013.050667.