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FANN Library - Version 2.0.0 Released

The new version of the fann library have been released. The new release includes automatic building and training of ANN's using the Cascade2 algorithm. The release includes many other changes as seen in the changelog for the release.

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. PHP, C++, .NET, Python, Delphi, Octave, Ruby, Pure Data and Mathematica bindings are available. A reference manual accompanies the library with examples and recommendations on how to use the library. A graphical user interface is also available for the library.

The complete list of features for the release is:
* 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 use reference manual, a 50+ page university report describing the implementation considerations etc. and an introduction article)
* 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 (simple train example and simple test example)
* 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 (licenced under LGPL)
* Framework for easy handling of training data sets
* Graphical Interface
* C++ Bindings
* PHP Extension
* Python Bindings
* Delphi Bindings
* .NET Bindings
* Mathematica Extension
* Octave Extension
* Ruby Bindings
* Pure Data Bindings
* RPM package
* Debian package

Posted by Steffen Nissen 2006-01-07