OpenNN
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| Developer(s) | Artelnics |
|---|---|
| Repository | |
| Operating system | Cross-platform |
| Type | Neural networks |
| License | LGPL |
| Website | www |
OpenNN (Open Neural Networks Library) is a software library written in the C++ programming language which implements neural networks, a main area of deep learning research.[1] The library is open-source, licensed under the GNU Lesser General Public License.
Characteristics
[edit]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 machine learning 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]
History
[edit]The development started in 2003 at the International Center for Numerical Methods in Engineering, 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. OpenNN is being developed by the startup company Artelnics.[4]
Applications
[edit]OpenNN is a general purpose artificial intelligence software package.[5] It uses machine learning techniques for solving predictive analytics tasks in different fields. For instance, the library has been applied in the engineering, energy, or chemistry sectors.[6][7][8]
See also
[edit]
- Comparison of deep learning software
- Neural Designer, also developed by Artelnics
- Artificial intelligence
- Machine learning
- Deep learning
- Artificial neural network
References
[edit]- ^ "OpenNN, An Open Source Library For Neural Networks". KDNuggets. June 2014.
- ^ 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.
- ^ "CORDIS - EU Research Project RAMFLOOD". European Commission. December 2004.
- ^ "Artelnics home page".
- ^ "Here Are 7 Thought-Provoking AI Software Packages For Your Info". Saurabh Singh. Archived from the original on 2014-06-27. Retrieved 25 June 2014.
- ^ R. Lopez; et al. (2008). "Neural Networks for Variational Problems in Engineering". International Journal for Numerical Methods in Engineering. 75 (11): 1341–1360. Bibcode:2008IJNME..75.1341L. doi:10.1002/nme.2304. hdl:10261/310317. S2CID 120913929.
- ^ P. Richter; et al. (2011). "Optimisation of Concentrating Solar Thermal Power Plants with Neural Networks". Adaptive and Natural Computing Algorithms. Lecture Notes in Computer Science. Vol. 6593. pp. 190–199. doi:10.1007/978-3-642-20282-7_20. ISBN 978-3-642-20281-0.
- ^ A.A. D’Archivio; et al. (2014). "Artificial Neural Network Prediction of Multilinear Gradient Retention in Reversed-Phase HPLC". Analytical and Bioanalytical Chemistry. 407 (4): 1–10. doi:10.1007/s00216-014-8317-3. PMID 25395205. S2CID 40461902.
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