AForge.Neuro

AForge.NET framework provides neural networks library, which contains set of classes aimed for creating different type of artificial neural networks and training them to solve certain tasks, like recognition, approximation, prediction, etc.

The library mainly allows users to create two categories of artificial neural networks: feed forward neural networks with activation function and one layer distance networks. Feed forward neural networks are represented as one layer or as multi-layer networks which don't have recurrent connections. Information flows in these networks from inputs to outputs passing all layers of neural network only one time without doing loops.

Neurons of such networks calculate their output by calculating weighted sum of their inputs and passing it to an activation function, whose value becomes an output of neuron.

With the ability to set an activation function to use in neural network and configure its size, it is possible to create different types of networks for different tasks starting from simple perceptron (neuron with threshold activation function), which does classification of linearly separable data into two classes and ends with complex multi-layer networks, which are aimed for recognition, prediction, etc.

One layer distance networks calculate their output as distance value between neuron's inputs and its weight - sum of absolute differences. These types of neurons' output calculation makes these networks usable as Kohonen Self Organizing Networks. Starting from 2.0.0 version (including beta) the AForge.NET Framework is published under LGPL v3 license.

The only exception is the AForge.Video.FFMPEG component which is published under GPL v3 license because it depends on the GPL build of the FFMPEG library. All previous releases of AForge.NET Framework (1.x.x) are published under GPL v3 license.