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OpenNN (Open Neural Networks Library) is a software library written in the C++ programming language which implements neural networks,[1] a main area of deep learning research. The library is open source, licensed under the GNU Lesser General Public License.

OpenNN
Developer(s) Artelnics
Repository github.com/Artelnics/OpenNN
Operating system Cross-platform
Type Neural networks
License LGPL
Website www.opennn.net

Contents

CharacteristicsEdit

The software implements any number of layers of non-linear processing units for supervised learning. This deep architecture allows the design of neural networks with universal approximation properties. Additionally, it allows multiprocessing programming by means of OpenMP, in order to increase computer performance.

OpenNN contains data mining algorithms as a bundle of functions. These can be embedded in other software tools, using an application programming interface, for the integration of the predictive analytics tasks. In this regard, a graphical user interface is missing but some functions can be supported by specific visualization tools.[2]

HistoryEdit

The development started in 2003 at the International Center for Numerical Methods in Engineering (CIMNE), within the research project funded by the European Union called RAMFLOOD (Risk Assessment and Management of FLOODs).[3] Then it continued as part of similar projects. At present, OpenNN is being developed by the startup company Artelnics.[4]

In 2014, Big Data Analytics Today rated OpenNN as the #1 brain inspired artificial intelligence project.[5] Also, during the same year, ToppersWorld selected OpenNN among the top 5 open source data mining tools.[6]

ApplicationsEdit

OpenNN is a general purpose artificial intelligence software package.[7] It uses machine learning techniques for solving data mining and predictive analytics tasks in different fields. For instance, the library has been applied in the engineering,[8] energy,[9] or chemistry[10] sectors.

See alsoEdit

ReferencesEdit

  1. ^ "OpenNN, An Open Source Library For Neural Networks". KDNuggets. June 2014. 
  2. ^ J. Mary Dallfin Bruxella; et al. (2014). "Categorization of Data Mining Tools Based on Their Types". International Journal of Computer Science and Mobile Computing. 3 (3): 445–452. 
  3. ^ "CORDIS - EU Research Project RAMFLOOD". European Commission. December 2004. 
  4. ^ "Artelnics home page". 
  5. ^ "Top 12 Brain Inspired Artificial Intelligence Projects". Big Data Analytics Today. October 2014. 
  6. ^ "Top 5 Open Source Data Mining Tools". ToppersWorld. November 2014. 
  7. ^ "Here Are 7 Thought-Provoking AI Software Packages For Your Info". Saurabh Singh. Retrieved 25 June 2014. 
  8. ^ R. Lopez; et al. (2008). "Neural Networks for Variational Problems in Engineering". International Journal for Numerical Methods in Engineering. 75 (11): 1341–1360. doi:10.1002/nme.2304. 
  9. ^ P. Richter; et al. (2011). "Optimisation of Concentrating Solar Thermal Power Plants with Neural Networks". Lecture Notes in Computer Science. 6593: 190–199. doi:10.1007/978-3-642-20282-7_20. 
  10. ^ A.A. D’Archivio; et al. (2014). "Artificial Neural Network Prediction of Multilinear Gradient Retention in Reversed-Phase HPLC". Analytical and Bioanalytical Chemistry. 407: 1–10. doi:10.1007/s00216-014-8317-3. 

External linksEdit