Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. Bindings to more than 15 programming languages are available. An easy to read introduction article and a reference manual accompanies the library with examples and recommendations on how to use the library. Several graphical user interfaces are also available for the library.

This software is also referenced in ORMS.

References in zbMATH (referenced in 7 articles )

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  1. Vasilis Nikolaidis: The nnlib2 library and nnlib2Rcpp R package for implementing neural networks (2021) not zbMATH
  2. Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
  3. Fabisch, Alexander; Kassahun, Yohannes; Wöhrle, Hendrik; Kirchner, Frank: Learning in compressed space (2013)
  4. Papa, João P.; Falcão, Alexandre X.; De Albuquerque, Victor Hugo C.; Tavares, João Manuel R. S.: Efficient supervised optimum-path forest classification for large datasets (2012) ioport
  5. Styrcz, Anna; Mrozek, Janusz; Mazur, Grzegorz: A neural-network controlled dynamic evolutionary scheme for global molecular geometry optimization (2011) ioport
  6. Aisa, Brad; Mingus, Brian; O’reilly, Randy: The emergent neural modeling system (2008) ioport
  7. Chen, Serena H.; Jakeman, Anthony J.; Norton, John P.: Artificial intelligence techniques: An introduction to their use for modelling environmental systems (2008)