Cognitively Inspired Neural Network for Recognition of Situations

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Author(s): Roman Ilin (Air Force Research Laboratory, Sensors Directorate, RYHE, USA)and Leonid Perlovsky (Air Force Research Laboratory, Sensors Directorate, RYHE, USA)
Copyright: 2012
Pages: 21
Source title: Nature-Inspired Computing Design, Development, and Applications
Source Author(s)/Editor(s): Leandro Nunes de Castro (Mackenzie University, Brazil)
DOI: 10.4018/978-1-4666-1574-8.ch002

Keywords: Biologically-Inspired Computing / Biotechnology and Bioinformatics / Engineering Science Reference / Medicine & Healthcare

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Abstract

The authors present a cognitively inspired mathematical learning framework called Neural Modeling Fields (NMF). They apply it to learning and recognition of situations composed of objects. NMF successfully overcomes the combinatorial complexity of associating subsets of objects with situations and demonstrates fast and reliable convergence. The implications of the current results for building multi-layered intelligent systems are also discussed.

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