OpenNN

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.{{cite journal | url=https://www.academia.edu/6491835 | title=Categorization of Data Mining Tools Based on Their Types | author=J. Mary Dallfin Bruxella| journal = International Journal of Computer Science and Mobile Computing | volume = 3 | issue = 3 | pages = 445–452 | year = 2014 |display-authors=etal}}

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).{{cite web|url=http://cordis.europa.eu/projects/rcn/67049_en.html |title=CORDIS - EU Research Project RAMFLOOD |publisher=European Commission | date=December 2004}} Then it continued as part of similar projects. OpenNN is being developed by the startup company Artelnics.{{cite web | url=http://www.artelnics.com |title=Artelnics home page}}

OpenNN is a general purpose artificial intelligence software package.{{cite web|url=http://www.efytimes.com/e1/fullnews.asp?edid=142005|title=Here Are 7 Thought-Provoking AI Software Packages For Your Info|publisher=Saurabh Singh|access-date=25 June 2014|archive-url=https://web.archive.org/web/20140627090333/http://efytimes.com/e1/fullnews.asp?edid=142005|archive-date=2014-06-27|url-status=dead}} 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.{{cite journal|title= Neural Networks for Variational Problems in Engineering | author = R. Lopez| journal = International Journal for Numerical Methods in Engineering | volume = 75 | issue = 11 | pages = 1341–1360 | year = 2008 | doi=10.1002/nme.2304|display-authors=etal| bibcode = 2008IJNME..75.1341L| s2cid = 120913929| url = https://www.scipedia.com/public/Lopez_et_al_2008a| hdl = 10261/310317| hdl-access = free}}{{cite book | chapter=Optimisation of Concentrating Solar Thermal Power Plants with Neural Networks | author = P. Richter| series = Lecture Notes in Computer Science | title = Adaptive and Natural Computing Algorithms | volume = 6593 | pages = 190–199 | year = 2011 | doi=10.1007/978-3-642-20282-7_20|display-authors=etal|isbn = 978-3-642-20281-0}}{{cite journal | title = Artificial Neural Network Prediction of Multilinear Gradient Retention in Reversed-Phase HPLC | journal = Analytical and Bioanalytical Chemistry | author= A.A. D’Archivio| pages = 1–10 | year= 2014 | doi=10.1007/s00216-014-8317-3 | pmid = 25395205 | volume=407|issue = 4| s2cid = 40461902 |display-authors=etal}}

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