LibTopoART is a software library providing platform independent C# implementations of several neural networks based on the TopoART architecture. This architecture has been developed as a unified machine learning approach tackling frequent problems arising in cognitive robotics and advanced machine learning, such as online-learning, incremental learning from data streams, as well as learning and prediction from non-stationary data, noisy data, imbalanced data, and incomplete data.

The base neural network TopoART (TA) is an incremental neural network combining elements of several other approaches, in particular, Adaptive Resonance Theory (ART) and topology learning networks. It is capable of parallel stable on-line clustering of stationary or non-stationary data at multiple levels of detail. These capabilities are complemented by derived neural networks dedicated to tasks such as classification, episodic clustering, and regression.


The implementations provided by LibTopoART differ in some minor aspects from the ones used in the original publications:

Besides TopoART, the current version of LibTopoART (v0.74) includes implementations of Episodic TopoART (episodic clustering of data streams), Hypersphere TopoART (clustering and topology-learning), TopoART-C (classification), and TopoART-R (regression). Future versions of LibTopoART will contain further neural networks based on TopoART.

LibTopoART is published under MIT licence. If you want to use it for scientific purposes, please cite the following paper:

Tscherepanow, Marko (2010). TopoART: A topology learning hierarchical ART network. In Proceedings of the International Conference on Artificial Neural Networks, LNCS 6354 (pp. 157–167). Berlin, Germany: Springer. (PDF)


version release date contains
v0.74 22 April 2017
  • two implementations (slow and precise / faster and less precise) of TopoART [1] [2] and TopoART-C [5]
  • implementations of Episodic TopoART [3], Hypersphere TopoART [4], and TopoART-R [6]
  • reference manual as a PDF file
  • several datasets (e.g., the datasets used for creating the figures above)
  • revised and extended samples
  • improved runtime performance and the usage of multithreading
  • R scripts for visualisation
  • compiled with Mono 4.8.1 for .NET 4.5