FANN

"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 20 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.

FANN Features:

• Multilayer Artificial Neural Network Library in C
• Backpropagation training (RPROP, Quickprop, Batch, Incremental)
• Evolving topology training which dynamically builds and trains the ANN (Cascade2)
• Easy to use (create, train and run an ANN with just three function calls)
• Fast (up to 150 times faster execution than other libraries)
• Versatile (possible to adjust many parameters and features on-the-fly)
• Well documented (An easy to read introduction article, a thorough reference manual, and a 50+ page university report describing the implementation considerations etc.)
• Cross-platform (configure script for linux and unix, dll files for windows, project files for MSVC++ and Borland compilers are also reported to work)
• Several different activation functions implemented (including stepwise linear functions for that extra bit of speed)
• Easy to save and load entire ANNs
• Several easy to use examples
• Can use both floating point and fixed point numbers (actually both float, double and int are available)
• Cache optimized (for that extra bit of speed)
• Open source, but can still be used in commercial applications (licenced under LGPL)
• Framework for easy handling of training data sets
• Graphical Interfaces
• Language Bindings to a large number of different programming languages
• Widely used (approximately 100 downloads a day)

http://leenissen.dk/fann/wp/

https://github.com/libfann/fann

https://github.com/search?utf8=%E2%9C%93&q=fann