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Cognitively Inspired Neural Network for Recognition of Situations

Author

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  • Roman Ilin

    (Air Force Research Laboratory, Sensors Directorate, RYHE, USA)

  • Leonid Perlovsky

    (Air Force Research Laboratory, Sensors Directorate, RYHE, USA)

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.

Suggested Citation

  • Roman Ilin & Leonid Perlovsky, 2010. "Cognitively Inspired Neural Network for Recognition of Situations," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 1(1), pages 36-55, January.
    • Handle: RePEc:igg:jncr00:v:1:y:2010:i:1:p:36-55
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