AForge.NET Framework
2.2.5 version is available!

The page provides the list of features available in the AForge.NET framework 2.1.2 version, which is currently available.

AForge.NET framework consists of several libraries, so below the framework's features are presented grouped by them:

AForge.Imaging, which is the biggest library of the framework so far, contains different image processing routines, which are aimed to help as in image enhancement/processing, as in some computer vision tasks:

AForge.Vision library consists of different motion detection and motion processing routines.

AForge.Math library contains different math related algorithms, which are used as internally by the framework, as can be used as they are by users. Some of the most interesting algorithm are:

AForge.Video library contains different classes, which provide access to video data. Nice to have it taking into account the amount of image processing stuff in the framework.

AForge.Robotics library contains some classes to manipulate some robotics kits:

AForge.Neuro library consists of some common neural network architectures' implementations and their learning algorithms:

  • Multi-layer feed forward networks utilizing activation function;
  • Distance networks (Kohonen SOM, for example);
  • Simple perceptron's learning, Delta rule learning, Back Propagation learning, Kohonen SOM learning, Evolutionary learning based on Genetic Algorithm;
  • Activation functions (threshold, sigmoid, bipolar sigmoid).

AForge.Genetic library consists of classes aimed to solve different tasks from Genetic Algorithms (GA), Genetic Programming (GP) and Gene Expression Programming (GEP) areas:

  • GA chromosomes (binary, short array, double array), GP tree based chromosome and GEP chromosome;
  • Selection algorithms (elite, roulette wheel, rank);
  • Common fitness functions (1/2D function optimization, symbolic regression, time series prediction).
  • Population class to handle chromosomes.

AForge.Fuzzy library consists of classes to perform different fuzzy computations, starting from using basic fuzzy sets and linguistic variables and continuing with complete inference system, which is capable of running set of fuzzy rules evaluating requested fuzzy variable.

AForge.MachineLearning library contains some classes from machine learning area:

  • QLearning and Sarsa learning algorithms;
  • Epsilon greedy, Boltzmann, Roulette wheel and Tabu Search exploration policies.

The AForge.NET framework contains also some more libraries/namespaces providing additional functionality, which is used by the framework, its samples or may be used directly in applications. Check AForge.NET framework's documentation to find all the information about all the classes available in the framework.