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Automatic buried mine detection using the maximum likelihoodadaptive neural system (MLANS), in Proceedings of ''Intelligent Control (ISIC)'', 1998. Held jointly with ''IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Intelligent Systems and Semiotics (ISAS)''</ref><ref>[http://www.mdatechnology.net/techprofile.aspx?id=227 ]: MDA Technology Applications Program web site</ref>
<ref>[http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4274797]: Cangelosi, A.; Tikhanoff, V.; Fontanari, J.F.; Hourdakis, E., Integrating Language and Cognition: A Cognitive Robotics Approach, Computational Intelligence Magazine, IEEE, Volume 2, Issue 3, Aug. 2007 Page(s):65 - 70</ref><ref>[http://spie.org/x648.xml?product_id=521387&showAbstracts=true&origin_id=x648]: Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III (Proceedings Volume), Editor(s): Edward M. Carapezza, Date: 15 September 2004,{{ISBN|978-0-8194-5326-6}}, See Chapter: ''Counter-terrorism threat prediction architecture''</ref>
This framework has been developed by [[Leonid Perlovsky]] at the [[AFRL]]. NMF is interpreted as a mathematical description of the mind's mechanisms, including [[concept]]s, [[emotions]], [[instincts]], [[imagination]], [[thinking]], and [[understanding]]. NMF is a multi-level, hetero-hierarchical system. At each level in NMF there are concept-models encapsulating the knowledge; they generate so-called top-down signals, interacting with input, bottom-up signals. These interactions are governed by dynamic equations, which drive concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input, bottom-up signals.
==Concept models and similarity measures==
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